The Consumer-Centric Knowledge Web — A Vision of Consumer Applications of Software Agent Technology — Enabling Consumer-Centric Knowledge-Based Computing

(This document is for historic reference — I wrote it on my now-defunct web site at http://agtivity.com/vision_of_consumer_applications_of_software_agent_technology.htm in 2005 and 2006, but hopefully at least some it it still has reasonable relevance. A companion slide show has apparently been captured by numerous web sites, such as: http://documents.mx/documents/consumer-centric-knowledge-web-a-vision-of-consumer-applications-of-software-agent-technology-enabling-consumer-centric-knowledge-based-computing-jack.html.)

Abstract

The Consumer-Centric Knowledge Web (CCKW) is a research proposal for a new knowledge-based computing infrastructure (global knowledge web platform) which will be much better-suited for consumer applications by enabling computer applications to finally understand what consumers really want. Consumers have the knowledge, but computers are too “dumb” to understand and there is no language for consumers to conveniently and comfortably express their knowledge. Software agent technology has the raw horsepower, but lacks the fuel (consumer knowledge) to go very far or very fast. Even the W3C Semantic Web is too week to provide consumers and software agents with enough power and ease of use. The CCKW will enable consumers and their software agents communicate in a manner that empowers both to “be all they can be”.

Caveats

  • This paper is a vision of the future and is not intended to portray a world that is “near”, “coming soon” or “right around the corner”. Many aspects of the vision will come to fruition to varying degrees over the next few years, but even a rough approximation of the full vision will occur only over a very extended period of time. In short, this vision is not “near”.
  • Which portions of this document should be refined and emphasized? Let us know
  • What “messages” make sense in a short overview article?
  • Should this be turned into a book? Would you buy it? Or at least read it?
  • Note: This paper in its current form is more of an “idea notebook” rather than a structured paper.
  • If there appears to be some inconsistency between sections, mostly that is due to evolution of my thinking as work has progressed.

Jump directly to PowerPoint presentation

Table of Contents

  1. Introduction
  2. Executive Summary
  3. PowerPoint Presentation
  4. Short Article on Enabling Consumer-Centric Knowledge-Based Computing
  5. A Modest Beginning
  6. An Immodest Clamor
  7. Why Start With Consumers?
  8. What do Users Want and Need?
  9. Consumer Problems to be Addressed
  10. Consumer-Centric Rather than Merely Consumer-Oriented
  11. Key Aspects of Consumer-Centric Computing
  12. What is Knowledge-Based Computing?
  13. Doesn’t the Semantic Web Do All of This Already?
  14. What is a Software Agent?
  15. Intelligent Agents
  16. Multi-Agent Systems
  17. Software Agents: Helping Consumers with Coping and Facilitating
  18. Knowledge-Based Software Agent Technology
  19. Consumer Agent Vision and Consumer Knowledge Agent Vision
  20. Why is Software Agent Technology Required?
  21. Trend from Software to Services to Software Agents
  22. Artificial Intelligence, Machine Intelligence, and Computational Intelligence
  23. Intelligence Augmentation
  24. Artificial Artificial Intelligence
  25. Multi-Mind or Group Mind
  26. Collective Intelligence
  27. Noosphere
  28. Neurosphere
  29. Knowledgesphere and Consumer Knowledgesphere
  30. Coping with Information Overload
  31. Facilitate Common-Sense Reasoning
  32. Software Agents as Consumer Proxies
  33. Coping with Information Fragmentation and Specialization
  34. Need for Calm Technology
  35. Radically Simplify the Computer Vocabulary Needed by Consumers
  36. Consumer Control and Controlling Authority
  37. Consumer-Centric
  38. Consumer Interests
  39. Intermediaries
  40. Environments
  41. Overlay Networks
  42. Semantic Web
  43. Implicit Semantic Web
  44. Web Services
  45. Knowledge Web
  46. Consumer Knowledge Web
  47. Text Mining, Data Mining, and Knowledge Mining vs. Semantic Web
  48. Grid Computing
  49. Semantic Grid
  50. Centralized Applications are Bad; Distributed Applications are Good
  51. Centralized Databases are Bad; Distributed Databases are Good
  52. Network-Centric Applications vs. Global Knowledge-Centric Applications
  53. Software as a Service (SaaS) vs. Software as Agents
  54. Ant Paradigm
  55. Swarm Intelligence
  56. Analogies to Plants, Forces, and Chemical Agents
  57. Consumer Knowledge Web
  58. Consumer Knowledge Web 1.0
  59. Consumer Knowledge Web vs. Consumer Web
  60. Consumer/Agent Knowledge Web
  61. Infrastructure: Toolkits, Frameworks, Middleware, Platforms
  62. Interaction Machines vs. Turing Machines
  63. Organic Application Development
  64. Mash-Ups as an Application Model
  65. Autonomic Operation
  66. Asynchronous Operation
  67. Radically New View of “A Program” or “An Application”
  68. Macro Software Agents and Micro Software Agents
  69. Network Effects
  70. Social Computing
  71. Human Computing
  72. Tribes and Social Values
  73. Dynamic Coalitions
  74. Virtual Communities
  75. Mobs and Smart Mobs
  76. Online Democracy
  77. Reputation and Trust
  78. Ethics and Deontic Logic
  79. Provenance
  80. Psychology of the Consumer Knowledge Web
  81. Consumer Attitudes Towards Knowledge
  82. Consumer Attitudes Towards Software Agents
  83. Consumer Attitudes Towards the Consumer Knowledge Web
  84. On-Demand Knowledge vs. Never-Need-to-Demand Knowledge
  85. Direction and the Journey Itself, Not the Destination
  86. Persistent Storage of Consumer Knowledge
  87. Elimination of Indirect Personal Information
  88. Organizing Consumer Knowledge
  89. Ontologies, Taxonomies, Tagging, Tagsonomies, and Folksonomies
  90. Difficulties with Directories and Taxonomies
  91. Auto-Directory, Auto-Taxonomy
  92. MyLifeBits Lifetime Store
  93. Knowledge Evolution
  94. Knowledge Feeds: Web Feeds and “RSS” for Distributing Knowledge
  95. Software Agent Feeds
  96. Social User Interfaces
  97. Scalable: Transcending Scale
  98. Open Source Software
  99. Open Data
  100. Facilitate Entertainment
  101. Economics
  102. Challenges
  103. What about Microsoft Bob?
  104. What About Ray Kurzweil’s “Singularity”?
  105. Consumer Use of Software Agents for Knowledge-Based Computing < 0.0001% of Kurzweil’s Singularity
  106. Computing Models
  107. Procedures vs. Tasks vs. Goals
  108. Values and Ideals and Life Goals
  109. Computing Infrastructure Will Vanish into Transparent Ubiquity
  110. Ultimate User Interface: Life Itself
  111. Retreat into the Background
  112. Location Awareness
  113. Ubiquitous Computing and Ambient Intelligence
  114. User Model of Software Agents
  115. More Deeply Satisfying than Toy-Like
  116. Toys, Games, Fun, and Play
  117. Potential Consumer Applications
  118. Potential Killer Apps
  119. Facilitate Barter
  120. GIS (Geographic Information System) Applications
  121. Architecture of Participation
  122. Consumer-to-Consumer Interactions (C2C)
  123. Consumer Networking and Social Networking
  124. Consumer Collaboration
  125. Implicit Collaboration
  126. Consumer Intelligence
  127. Life Mentor
  128. Life Agents
  129. Lifelong Learning
  130. Role in Education
  131. Empowerment
  132. Facilitate Leadership
  133. Facilitate Creativity
  134. Facilitate Imagination
  135. Facilitate Dreaming and Aspiring
  136. Facilitate Natural Language Interfaces
  137. Overcome Language Barriers
  138. Opt-In is the Law
  139. Early Adopters: High-end (Professionals) and Low-end (Kids)
  140. Handheld Applications and Mobile Applications
  141. Mobile Environments
  142. Mobile-Agent Applications
  143. Management of Consumer Data
  144. DVPC: Distributed Virtual Personal Computer
  145. Virtual Networked Bits
  146. Sharing of Consumer Data, Information, and Knowledge
  147. Consumer-Centric File System — Consumer-Centric Knowledge Organizer
  148. Management of Medical Records
  149. Health, Nutrition, and Medical Applications
  150. Legal Applications, Management of Legal Information
  151. Coping With Uncertainty
  152. Facilitating Roles and Personae
  153. Learning to Learn
  154. Auto-Search, Intelligent Search Alerts and Notification
  155. Semantic Search: Deep Context vs. Simple Keywords
  156. Go to Google vs. My Agents are on It
  157. Searching vs. Sleuthing
  158. GMWIMW — Give Me What I Might Want
  159. Thinking Outside the Box
  160. Out of the Blue
  161. Peer-to-Peer (P2P), Agent-to-Agent (A2A) Metaphor
  162. Robots
  163. Mass Customization
  164. Blogging and the Blogosphere
  165. Auto-Blogging
  166. Mobile-Phone Applications
  167. Fuzzy Logic
  168. Constraint Management to Automate Functions
  169. Psychology Applied to Software Agents
  170. Spam and Irrelevant Knowledge and Useless Information
  171. Identity and Anonymity
  172. Digital Identity
  173. Identity Theft
  174. Identity Union
  175. Privacy
  176. Security
  177. Big Brother
  178. Law Enforcement
  179. Terrorism
  180. Information Warfare
  181. Levels of Autonomy
  182. Social Structures
  183. Consumer-Centric Tools
  184. Never a Need to Agree to “Trust Us”
  185. Human-Like Interface
  186. Intellectual Property: Enabler and Obstacle
  187. Legal Aspects
  188. Architecture
  189. Software Agents in Fiction
  190. What’s Next
  191. The Plan
  192. Where to Start
  193. References
  194. Raw Notes and TBD

Introduction

Software agent technology has been an active field of research for more than a decade. Although there have been limited applications of the technology for consumer use, deeper success has been achieved in industrial applications. There have been numerous false starts to commercialize agent technology on a wide spread basis, including for consumers, but alas the hype has greatly exceeded both the capabilities of off-the-shelf technology and our own abilities. With every passing year we remain on the cusp of finally breaking out and fielding the kind of technological breakthroughs which will finally make the consumer application of software agent technology a reality.

Please note that the intention of this vision of software agent technology is not to turn the computer into a human-like robot, but simply to enable the computer as a competent assistant in the lives of consumers. The goal is not to pursue artificial intelligence per se, but to incorporate those aspects of AI which relate to agency, where the consumer decides what responsibilities to delegate and is the controlling authority for goals to be pursued by their software agents.

The focus of this vision is not to preview the totality of consumer applications that could be constructed, but to establish a base vision upon which consumer applications can then be envisioned. Alternatively, this vision can be considered as the model for a platform upon which consumer applications can be built.

Central to a new wave of consumer-centric computing is support for interactions that are based on higher-level knowledge rather than simply moving information from one location to another. The goal of using software agent technology is to enable knowledge-based computing.

Executive Summary

Simple agent-like features have found their way into many consumer applications, but if you think of software agent technology as a range of mountains, efforts to-date have merely probed into the lower foothills surrounding the mountains.

Existing use of agent-like technology is essentially no more than agent-light, or a relatively thin veneer that only modestly approximates the full potential of software agent technology.

Significant resource allocations will be needed to push much higher in those foothills.

Much basic research is needed to enable significantly higher climbs in the mountains of software agent technology.

Some of the ascent can be made without resorting to deep artificial intelligence (strong AI), but at some stage AI will be needed.

As central as software agents are to this vision of computing, agents are simply the messengers, and the heart and soul of the new messages is higher-level knowledge. But agents have a three-fold mission: 1) move the knowledge around, 2) facilitate the higher-level processing of knowledge, and 3) monitoring and assuring that knowledge is used effectively.

In summary, we need to fund lots of basic research, as well as advanced development labs where the results research can tested without all of the associated market risk that goes with traditional product development.

The intention is not to do research for the sake of research, but to lay the foundation for a quantum leap in improvement of consumer-oriented computing capabilities.

PowerPoint Presentation

For a brief executive-level overview of the Consumer-Centric Knowledge Web, see the Consumer-Centric Knowledge Web PowerPoint presentation.

Short Article on Enabling Consumer-Centric Knowledge-Based Computing

TBD

Something a bit more sophisticated than what would appear in the popular press, but less dense than what would appear in a refereed technical journal. Approximately three to five pages. Plus three or four diagrams.

Emphasize a few scenarios demonstrating benefit to consumers.

Emphasize focus on a platform for knowledge-based applications rather than specific applications.

A Modest Beginning

Existing applications of software agent technology to consumer applications have been quite modest to date and simply agent-like than truly agent-oriented :

  • News Filters and News Agents and News Alerts
  • Search Engines and Search Alerts
  • Buying Agents or Shopping Bots
  • Collaborative Recommendation
  • Email Agents and email list servers
  • Email Robots (e.g., Auto-Responders)
  • Anti-Virus Agents, Anti-Spam Agents, Anti-AdWare Agents, Anti-SpyWare Agents, and Anti-MalWare Agents
  • Web Browsing
  • Chatterbots and Conversational Interactive Agents
  • Auction Systems and Auction Agents
  • Electronic Marketplaces
  • eCommerce
  • On-the-fly Spelling and Grammar Checkers
  • Information Alerts (e.g., by registering at a web site or on an email “listserv”)
  • Avatars, Animation, Virtual Reality, and Gaming Environments
  • Chat rooms, discussion forums, email lists, virtual communities
  • System and Application Activity Monitors
  • Peer-to-Peer (P2P) File Sharing
  • Consumer-oriented robots (e.g., Roomba and Sony’s Aibo dog)
  • Stock trading alerts and triggers
  • Automated backup and archiving of files
  • Background spelling and grammar checking and auto-correction
  • History recall and auto-fill-in of form fields

Such agents either perform very simple tasks or require extraordinary effort on the part of the user. There has been little evidence of what can be called intelligence or even deep understanding of user needs.

The basic problem is that attempts at “intelligence” tend to merely mimic human intelligence, and very poorly at that.

An Immodest Clamor

Back around 1997, quite a number of rather prominent researchers and entrepreneurs loudly proclaimed that we finally had all the technological elements that could be assembled off the shelf to finally realize the promise of artificial intelligence in the form of software agents or intelligent agents and something called a Knowledge Web. Unfortunately, they were wrong, and very wrong at that.

Take a look at some of the outrageous comments in the announcement for the Agents’97 conference. Some of this stuff will come to pass, eventually, but even eight years later we seem not only no closer, but the objective seems rather more distant.

Another example of the contemporary thinking back in 1996 is the thesis of Björn Hermans entitled “Intelligent Software Agents on the Internet: an inventory of currently offered functionality in the information society & a prediction of (near-)future developments”.

Sure, a number of elements are in fact available, but far short the the kind of critical mass that is needed to really bring software agents into the mainstream.

I was in fact one of the people who fell for this hype back in 1997. My interest back then was mobile software agents or the ability for a running program to relocate itself to a different host machine. I gave up on that metaphor rather quickly once I realized the problems and obstacles, but at least it opened my eyes to the true long-term potential for the agent metaphor even if there was no short-term rainbow to the pot of gold.

Even in 2000, Danny Hillis of Thinking Machines fame claimed that “The knowledge web is an idea whose time has come.” Here we are in 2006, and still we don’t have even a hint of a working Knowledge Web.

The immodest clamor has since died down, with the focus now on agent technology and multi-agent systems, with much less emphasis on intelligence, except as a pure artificial intelligence research topic, where it belongs, for now.

Why Start With Consumers?

Although high-end corporate information technology applications may seem like a better place to initially focus the application of software agent technology, my view is that corporate needs are more sophisticated, complicated, and demanding. It would seem far better to focus on deploying a simplified vision of user-oriented intelligent software agents and knowledge processing out in the consumer space and then attempt to beef up the technology to meet more stringent corporate demands. This is the model of how the PC and personal computing software evolved, and it seems like the most obvious success model to emulate.

In truth, the PC and its software did not start from scratch, but simply scaled down what was available with mainframes and minicomputers. Similarly, a lot of research and some preliminary commercial work has been done for software agents and knowledge processing. It’s not very usable, even by corporate user, but at least there is a good starting point, analogous to the PC.

The overall model, as with any advanced technology, is to first try to apply the technology to high-end government, military, space, and commercial applications, meet limited success there, push a stripped-down version of the technology down to consumers, beef up the technology to the point of an interesting level of consumer acceptance, and then beef up the technology to finally meet the true needs for high-end government, military, space, and commercial applications.

What do Users Want and Need?

That is the question. Or, more to the point, how can a computer software program best gain insight into what the user wants and needs?

The artificial intelligence guys have something called the BDI model, Beliefs, Desires, and Intentions. That’s essentially the totality of what the user has in their heads and what software agents need to know to do an even passable job of satisfying the user.

Yeah, ultimately software agents quite literally need to be able to read the user’s mind, but that is still a pipe dream or at least needs to wait for Ray Kurzweil’s “singularity”.

Users need easy to use tools that allow them to build up a personal database in which they can build up and maintain their own knowledge base of their personal beliefs, desires, and intentions. Once such a knowledgebase is in place, software agents can query it to effectively “read” the user’s mind.

Consumer Problems to be Addressed

Specifically, a consumer-centric knowledge web needs to fix the following problems:

  1. Search engines…
  2. Don’t know the meaning of the web pages that they crawl and index.
  3. Don’t know the meaning of the keywords used by the consumer.
  4. Don’t know the context of what the consumer is trying to accomplish.
  5. Consumer information is not kept private and under complete control of the consumer.
  6. Web is vendor-centric, rather than putting the consumer in absolute control.
  7. The Web and web sites and services don’t have even a basic comprehension of common sense concepts.
  8. Potential collaborators for consumers cannot be found easily.
  9. The Web (and Semantic Web) don’t do near enough to encourage broader and deeper consumer-to-consumer (C2C) interactions.
  10. Software agents can’t do a lot for consumers since they don’t know what the consumer is trying to do.
  11. Consumers can’t depend on dumb software agents.
  12. Consumers have no way to intelligently communicate with intelligent software agents.
  13. Consumers have to explain too much to web sites and services since there is no mechanism for the consumer to express knowledge about themselves in a way that can be easily communicated to sites and services.
  14. The overall interface between consumers and vendors is too dumb.
  15. Consumers are unable to communicate with other consumers who do not speak their language.
  16. Mechanisms for expressing information are too primitive.
  17. Most Web interactions are task-oriented rather than permitting the consumer to express goals.
  18. Most Web functions require consumer interaction, rather than permitting a consumer to tell intelligent software agents to pursue a variety of goals on their behalf in an autonomous manner.
  19. Web interfaces are quite noisy and distracting, rather than being based on calm technology.
  20. No easy way to enable the software agents of different consumers interact directly on behalf of all of those consumers.
  21. Consumers and software agents don’t have a common language for conveniently and comfortably communicating information, beliefs, desires, and intentions.
  22. No reliable mechanism for storing information and knowledge for consumers.
  23. The Web, and even the Semantic Web, are focused on manipulation of traditional computer information (numbers, text, images, audio, video), with virtually no accommodation for true knowledge. Most applications of even the Semantic Web have focused on “meaning” of information(in the sense of a traditional SQL database), rather than on meaning in the sense of how real people communicate.
  24. Simply put: The computer has no clue what the consumer means or what information means to the consumer.
  25. Traditional knowledge management tools (even advanced ones like Cyc) are far too cumbersome and yet far too primitive to be used by consumers to conveniently and comfortably express and manipulate true knowledge.
  26. Handheld computing devices are too difficult to use on-the-run with dumb, information-intensive user interfaces. Knowledge-based and agent-based interfaces can reduce the information display and entry requirements.
  27. etc…

Consumer-Centric Rather than Merely Consumer-Oriented

Many applications available today are consumer-oriented, meaning that vendors and organizations have designed their software to appeal to consumers. The vision espoused in this paper is for a quantum-leap forward in computer software applications which will be consumer-centric rather than consumer-oriented. The difference is the question of who is in control, the vendor or the consumer. Many vendors have done a passable job of appealing to the needs of consumers, but that is not even close to being far enough to support the vision of consumer control and knowledge-based applications that we think is feasible.

Consumer-oriented approaches are acts of reaching out to and controlling consumers by vendors, but consumer-centric approaches focus on consumers being in control or both the game and their own destiny.

Key Aspects of Consumer-Centric Computing

  1. Consumer is at the center and in control
  2. Knowledge-based
  3. Calm Technology
  4. Automation is more important than highly interactive user interfaces
  5. Knowledge-based software agent technology
  6. Interaction with other consumers
  7. Maintaining privacy while enabling and encouraging sharing and collaborative behavior
  8. Network effects of consumer-to-consumer interactions
  9. Exploiting group and global knowledge

What is Knowledge-Based Computing?

Knowledge-based computing focuses on aligning information processing as close as possible to the level of knowledge which the consumer works with, allowing the consumer to express themselves to the computer as closely as possible to how they would express themselves to other people. Rather the immediately translating the consumer’s knowledge into a low-level information format, the goal is to keep the knowledge in a higher-level knowledge-oriented form as often as possible.

Doesn’t the Semantic Web Do All of This Already?

As you are reading some of this you may hear yourself and others asking a very important question: Doesn’t the Semantic Web do all of this already? In short: No. If you fully digest the entire vision presented here and compare it to a full digesting of the reality of the Semantic Web (as espoused in the May 2001 article in the Scientific American), you will see that the Semantic Web comes up far short. The Semantic Web is a significant leap forward, but simply is not about knowledge-based computing, consumer or otherwise. The Semantic Web is about information-based computing, and maybe someday, after significant research, be extended to grasp real and meaningful knowledge, but for today and the next few years the Semantic Web is primarily about representing traditional IT-style information in ways that IT-style computer programs can process it, as opposed to the old Web in which information was displayed as raw text and raw graphics, with no clues to computer programs as to the structured information that was being presented on an HTML web page.

Put simply, the vast bulk of the information represented in the Semantic Web is hardly more than the level of information that would be stored in an SQL-style IT database. In fact, much of the information on the Semantic Web actually is sourced from SQL-style IT databases.

Much of the so-called knowledge that is supported by the current Semantic Web is still only a representation of knowledge as an aggregated knowledge artifact (e.g., a block of text in a natural language) rather than drilling down and representing details of true, human meaning. For exampleblogs in the form of XML-based web feeds have a significant amount of machine-processable information, and that is indeed a significant technological advance, but the title and body of the blog post are still uninterpreted blocks of text in a natural language, or maybe not, as the case might be.

A portion of the Semantic Web relates to services performed on the Internet, and is referred to as Semantic Web Services (SWS). SWS is a significant step forward compared to traditional communications with server-based applications and Web-based applications, but still works at the level ofinformation or even structured information of the traditional IT-style, and doesn’t even come close to getting into meaningful knowledge. SWS also has a rather simplistic approach to “agents”, and doesn’t even begin to put a dent in what it means to be or support an intelligent agent, let alone vast swarms of agents with emergent behavior, and how mere mortal users might convey human knowledge to agents and how agent can convey machine knowledge to humans.

The transformation between human knowledge and machine knowledge is a vast, unresolved research problem. At present, no relatively simple mechanical solution easily implemented with off-the-shelf technology is capable of readily transforming to and from human knowledge. The vision of this paper is that tools and techniques can be developed to facilitate the knowledge transformation process, but that much research is required. And the prospect of vast armies of knowledge engineers standing by to manually encode human knowledge into XML/RDF documents is currently a non-starter. Constructing ontologies for even very simple domains is still quite tedious, very error-prone, and incomprehensible to mere mortals.

Proponents of the Semantic Web pay lip service to the importance of ontology, or how one goes about completely specifying any domain of knowledge. As the Scientific American article refers to ontology, “Artificial-intelligence and Web researchers have co-opted the term for their own jargon, and for them an ontology is a document or file that formally defines the relations among terms. The most typical kind of ontology for the Web has a taxonomy and a set of inference rules.” That’s hardly sufficient for representing hard-core, meaningful knowledge that humans, users, even consumers can relate to. The article neglected to mention that AI and Web researchers have “co-opted” the term taxonomy as well. In fact, their usage of the term taxonomy belies the truth about so-called ontologies for the Semantic Web: they’re hardly more than data declarations and schemas and business process rules in the traditional IT sense and are essentially discussed as such in the article. To represent meaningful knowledge of the sort relevant to the interests of consumers, we’ll need techniques a little more powerful and more flexible and more easer to use than simple rules, business rules, or even so-called inference rules.

Now, it may turn out that our vision of a Consumer-Centric Knowledge Web can be built on top of the Semantic Web, and it would in fact be wonderful if the effort to achieve our vision is greatly reducing by the existing Semantic Web technologies, but that is not a requirement, nor is it a given, nor is it even a likelihood. Far too much research remains to be done to prejudge the extent to which the Semantic Web will be reusable enough to support a full-blown, meaningful knowledge web.

A more elaborate argument can be made about the differences between the current vision of the Semantic Web and our vision of a Consumer-Centric Knowledge Web, but the main point remains that if you read any of this and think that “all of that is already done in the current Semantic Web”, then I would suggest that you go back and read more carefully and challenge your own assumptions.

To summarize, the Semantic Web does indeed have a bright and prosperous future, but as presently envisioned, it won’t achieve the goals espoused by the vision presented here for a Consumer-Centric Knowledge Web.

What is a Software Agent?

What is a software agent? That question is a matter of great debate, but the essence is that a software agent is a computer program which possesses the characteristic of agency, that it is acting on behalf of another entity (i.e., the consumer) in pursuit of goals specified or controlled by that other entity (the consumer).

The key qualities are that software agents are performing tasks and working towards goals for the consumer, without the need for the consumer to be involved and worried about every step of the way. This implies a degree of knowledge about the consumer and intelligence about how to work on the consumer’s behalf. It is necessary but not sufficient to know what consumers in general want, but also to deeply comprehend what each particular consumer wants.

Intelligent Agents

The long-term goal is that software agents will take on more of the attributes that we associate with intelligence. In the interim, so-called intelligent agents will evolve gradually towards a sense of human-like intelligence, but remain more focused for now on more of a mechanical, drone-like mode of operation that at best mimics human intelligence. Even in the longer run, intelligent agents will converge on what should be called computational intelligence or machine intelligence that will continue to fall short of true human-level intelligence in many ways even as it surpasseshuman intelligence in other ways.

Multi-Agent Systems

Researchers in the field of Artificial Intelligence (AI) have long viewed multi-agent systems (MAS) as a very promising model for mimicking bother the human mind and communities of autonomous individuals. Traditional multi-agent systems have been closed and quite limited in scope, but gradually they have been becoming more open and flexible. Many of the approaches to the interaction of software agents on the Internet have been based on research in multi-agent systems. Much more research is needed, but at least some of the foundation has already been laid.

The biggest open research topics relate to how to apply MAS concepts to free-willed (and free-wheeling) consumers as opposed to more mechanical and drone-like industrial applications.

Knowledge-Based Software Agent Technology

Knowledge-based software agent technology blends the deep richness of a knowledgebase and deep semantic meaning with the raw power of software agent technology. It is the combination of both that provides the breadth and depth needed to enable computer software to truly understand and provide support for what the consumer is really trying to do.

Consumer Agent Vision and Consumer Knowledge Agent Vision

To simplify the terminology a little, this overall white paper can be thought of as referring to a consumer agent vision or a consumer knowledge agent vision. The term consumer agent should be good enough, but there is enough ambiguity that we should settle on the term consumer knowledge agent. The latter seems to capture all three essential ingredients of the vision of this paper: consumers, their knowledge, and using software agents to facilitate the growth and use of that knowledge. As a technicality, the full term is consumer knowledge-based software agent, but can also be referred to as a consumer knowledge-based agent.

Software Agents: Helping Consumers with Coping and Facilitating

The two big categories of support that software agents can provide for consumers are coping and facilitating. Consumers either have an idea or goal that they are interested in pursuing and need assistance in facilitating that idea or goal, or they are confronted with a problem or task or issue that they are not particularly interested in pursuing, but they have no real choice, so they need help coping with the problem, task, or issue. Consumers need a lot of support, and software agent technology seems ideally capable of providing a significant amount of it.

Why is Software Agent Technology Required?

To date, nobody has come up with a technology that scales up as well as webs of interconnected software agents. They are more flexible. They can automatically adapt to constant changes in a dynamic networking environment. They can evolve and support applications that are evolving. Hand-coding distributed applications is simply too tedious, too inflexible, and too error-prone for large-scale distributed applications. System administration for such large-scale applications and databases is simply beyond the capabilities of human system administrators. Large scale distributed applications will become too important to entrust to traditional, ad-hoc, error-prone approaches to network design.

Trend from Software to Services to Software Agents

Today the rage is about the transition from software to services, particularly Web Services. The vision espoused in this paper concerns a future evolution of the same trend, but where “traditional” Web Service-based applications will give way to applications based on software agent technology. Web Services will continue to play an important role, but the vast bulk of the activity will be based on the interactions of autonomous software agents, with Web Services being background resources available for use by software agents.

Artificial Intelligence, Machine Intelligence, and Computational Intelligence

A tremendous amount of research has been performed on the topic of artificial intelligence (AI) over the past 50 years. Software agent technology draws on this body of research, but much research remains to be pursued. Although AI classically focuses on the holy grail of human-like intelligence, it is more sensible to work in the direction of computational intelligence or machine intelligence which aims to at best mimic human intelligence as feasible, but to go far beyond limited human intelligence in as many areas as possible.

Intelligence Augmentation

Even when the best AI techniques cannot begin to approach human intelligence, there is great promise to the concept of intelligence augmentation, where the aim is to blend a hybrid of human and machine intelligence, with each side contributing its best efforts. With software agent technology we’re looking at leveraging the contribution of the human consumer with the “intelligent” efforts of a potentially very large number of software agents, and coupling that with the efforts of other consumers and their software agents as well.

Artificial Artificial Intelligence

Artificial artificial intelligence (AAI) refers to intelligence augmentation where human beings volunteer to perform tasks at the behest of computer software, especially in situations where true artificial intelligence simply isn’t up to the task. This capability further extends the power of software agent technology, and software agents can be used to facilitate AAI itself. The consumer won’t even be aware (in general) that any humans are in the loop.

Multi-Mind or Group Mind

Consistent with the thinking behind the old adage “two heads are better than one”, software agents have the potential to act as intermediaries and facilitators between consumers so that a group of consumers can interact and act as if they had a much larger multi-mind or group mind. The leveraging that software agents can provide could lead to a dramatic boost in productivity and innovation and a host of social benefits.

Collective Intelligence

One of the network effects of consumer collaboration is that collectively a group of consumers can appear to have a level of intelligence greater than any of the individuals of the group. Again, software agent technology fulfills a major role in the collaboration process and facilitates the communication of knowledge among the members of the group. Further, agents can collect and process knowledge on the behalf of the consumers, according to the interests of the consumers in a far more efficient manner than the consumers themselves. By tapping into the shared knowledge of the group, the software agents acting on behalf of the group members can effect a collective intelligence that benefits the group as a whole, and the individual consumers as well.

Collective thought can be a powerful tool both for the members of the community doing the thinking, but also for the community overall. Organizing collective thought in a consumer knowledge web would be a good first step at leveraging all of that collective thought.

Collective thought is actually quite tedious if attempted manually (e.g., exchanging and reading documents), but can be greatly facilitated using software agent technology to do much of the collection, storage, correlation, and more efficient distribution of the knowledge that each member of the group needs to come up to speed with the thinking of the full group

Noosphere

The noosphere is the composite of all interacting minds. The concepts of multi-mind and group mind would be specific subsets of the overall, global noosphere.

Neurosphere

Although the term neurosphere can be treated as synonymous with noosphere in some contexts, it really includes the use of the Internet as enabling the group mind. The term has been popularized by Donald Dulchinos in his book “Neurosphere: The Convergence of Evolution, Group Mind, and the Internet”.

Knowledgesphere and Consumer Knowledgesphere

The knowledgesphere is analogous to the noosphere, but simply refers to the total knowledge within any particular environment. So, we could speak of the Web knowledgesphere, the total knowledge on the Web, or the knowledgesphere of a particular group of individuals. In the context of this paper, “the” knowledgesphere is the consumer knowledgesphere which is the total knowledge accessible by the software agents which are working on the behalf of consumers.

Coping with Information Overload

Even with sophisticated search engines, there is already far too much information out on the web for the average consumer to easily find the information that best meets their needs. Software agent technology coupled with a comprehensive knowledgebase relating to the interests and behavior of the consumer will provide a rich level of context to greatly facilitate navigation through the haystack to quickly find the needles of interest to the consumer.

Facilitate Common-Sense Reasoning

As real-world situations get more complex, even simple reasoning can become quite difficult. The vast knowledge embodied in the consumer-centric knowledge web, coupled with software agent technology can render assistance, helping to drill down and reach out to simplify reasoning in even very complex scenarios. Often, the problem is simply that the consumer doesn’t have the appropriate knowledge immediately at their finger-tips, or doesn’t have knowledge of paths or chains of reasoning that can help them or guide them to their goals. Much research is required, but the potential benefits are huge.

Software Agents as Consumer Proxies

There are many situations where the consumer is simply too busy or distressed or finds it inconvenient or uncomfortable to take an action by themselves and may elect to have a proxy act on their behalf. Software agents can be a very appropriate choice for supporting the concept of a proxy, giving the user control without the burden of the actual actions. The important thing is that the software agent must have access to enough knowledge about the consumer and their interests so that the agent can act appropriately without detailed, tedious, and error-prone instruction from the consumer.

Coping with Information Fragmentation and Specialization

As information technology has progressed and evolved, information has gotten more refined, but more fragmented and exceedingly more detailed specializations have emerged. This information fragmentation and information specialization has worked to the detriment of most consumers. Sure, more choices have become available, but navigating and discovering and exploiting those choices has gotten far more difficult. This is a prime reason why we need to make the leap from information to knowledge, and a prime reason why we need to exploit the power of software agent technology.

Need for Calm Technology

Calm technology has the ability to make itself available to consumers and work on their behalf without significantly disturbing their sense of calm. A side effect is that more technologies can be exploited by the consumer without dragging them down and making them feel that they are overburdened. This needs to be a key criteria for new technologies to be introduced into the consumer domain. Software agent technology, especially the capability of executing in an autonomous manner without intervention or direct control of the consumer, is almost inherently a calm technology, if designed and deployed properly.

Radically Simplify the Computer Vocabulary Needed by Consumers

Knowledge-based software agent technology can radically improve the degree of automation of the consumer’s personal computer (or other access device. The effect is to radically simplify computer vocabulary needed by consumers. Much of the jargon can be eliminated from the consumer’s vocabulary. No longer will consumers need to fret over install, setup, configure, settings, options, tuning, troubleshooting, tech support, training, etc.

Consumer Control and Controlling Authority

A central requirement for consumer applications is that the consumer is in control, not some vendor or service provider, but the consumer themselves.

Software agents add the twist that since the software agents themselves are technically “in control” at any moment, it is sufficient that the consumer is the controlling authority.

Consumer-Centric

Current online networks tend to be vendor-centric or server-centric or net-centric, but software agent technology enables the consumer to be placed at the center of attention. This consumer-centric approach simultaneously serves the needs of the consumer, and also enables vendors to more effectively interact with consumers.

Consumer Interests

Much of what a consumer will do which any computer software is driven by their interests, suggesting that software agents can help consumers a lot by providing rich support for consumer interests, whether that be collecting consumer interests, organizing them, searching for them, matching with the interests of other consumers, or whatever, the point is that consumer interests need to be a key aspect of the Consumer-Centric Knowledge Web.

Software agent technology can facilitate how consumers conceptualize, think about, and express their interests. One of the big problems today is that computer software applications have few clues about the real interests of the consumer, and hence can offer rather little assistance.

Intermediaries

The important aspect of a software agent is that it is an intermediary, acting on resources and acting with other entities in order to achieve goals that were set by the controlling entity or controlling authority, the principal of the agent or the agent principal.

The entities that a software agent interacts with may be either principals acting on their own behalf or other software agents acting on behalf of their principals.

In any case, the heart and soul of software agency is that users or consumers are in need of services that are available, but they benefit greatly through the use of intermediaries, agents, which facilitate interactions.

Environments

Just as important as the software agents themselves are the environments in which the agents operate, analogous to vehicles and roads and highways.

We presume that the Internet and the Web will be the primary environments of interest for consumer software agents. But the consumer’s personal computer or access device is itself a full environment. A P2P community is a distinct environment. Any overlay network could be a distinct environment in which software agents can operate.

Mobile phones, Bluetooth-accessible devices, and even freely-roaming robots can also be parts of environments for software agents.

Environments provide resources and services that software agents can utilize in pursuit of goals.

Environments present opportunities for software agents, but they can also present threats in the form of malicious agents.

Overlay Networks

An overlay network is a dynamic collection of network nodes that act as a subset of the entire collection of nodes in the network. A file-sharing network is an example of an overlay network. Overlay networks are an excellent infrastructure for supporting dynamic online communities, as well as the software agents which support such online communities.

Semantic Web

Not to be confused with web services, the Semantic Web offers a guiding philosophy of a rich network of semantic data that can be processed in an automated manner by software comparable to software agent technology. Every consumer and every product and service vendor could have richly-hyperlinked semantic, machine-comprehensible information at the level associated with knowledge that can enable software agents to offer services far beyond what any single vendor or tightly-knit collection of vendors might offer.

The semantic web is the ocean and continents through which and across which software agents will navigate in pursuit of satisfying the needs, interests, goals, and ideals of the consumers who control those agents.

A key aspect of the semantic web is that software agents will be able to continuously scan the dynamically varying content of the semantic web and continuously computing patterns than can be used by software agents to offer semantic services to consumers and vendors alike.

Implicit Semantic Web

A rich semantic web is quite valuable, but very difficult to produce if constructed manually. Rather, we need tools which will implicitly add knowledge to the semantic web as it becomes known by intelligent software agents as those agents perform tasks on behalf of consumers. Each action or choice carried out by a software agent for a consumer makes additional knowledge available to be added to the semantic web. This implicit semantic web can quickly grow to be orders of magnitude larger than any manually constructed semantic network.

The implicit semantic web will be filled with structured representations of the knowledge and behavior of the the many consumers and vendors who participate in the semantic web.

To be useful, knowledge must be available in both its detailed form and its abstracted form. The implicit semantic web would support both.

By dramatically increasing the size of the available knowledgebase, finer and broader and deeper patterns will become available to the software agents that provide applications to consumers and vendors alike.

Web Services

Not to be confused with the Semantic Web, the concept of Web Services is a more powerful and open approach for vendors to offer services on the Internet. Enough thought has been given to the design of the technical standards that underpin Web Services so that they are flexible enough to support a global networking of services that has the potential to result in more dramatic network effects and economies of scale. Although software agents will tend to interact and communicate among themselves, Web Services provides a rich and flexible interface that will enable software agents to access more traditional forms of services offered by traditional vendors.

Over time, Web Services themselves will evolve more towards the agent-oriented approach to computing. Either way, software agent technology will shield and insulate consumers from the idiosyncrasies of the underlying technology.

Knowledge Web

A knowledge web is a portion of the Semantic Web which focuses on knowledge. A knowledge web is far more than a static collection of encoded knowledge. Knowledge is created constantly, including through processes and services that are active at any moment. Software agents will be key participants in both supporting knowledge webs, and the generation of new knowledge. A knowledge web should be thought of as not simply a repository of information, but a platform for knowledge-based applications.

Consumer-Centric Knowledge Web

A consumer-centric knowledge web is a knowledge web which focuses on knowledge that is both of interest to consumers and controlled by consumers. There is certainly a substantial gray area between all knowledge and consumer-centric knowledge, but it is the knowledge-oriented processes that are important, including a bias towards the interests of consumer. A consumer knowledge web is a platform for consumer-centric knowledge-based applications.

Text Mining, Data Mining, and Knowledge Mining vs. Semantic Web

One of the ongoing debates is over gathering knowledge through data mining (mined knowledge) versus explicitly-constructed knowledge. Specifically, should we have to wait for everyone to convert to explicit knowledge structures represented as the Semantic Web, or can sufficient knowledge structures be automatically generated as a result of text mining, data mining, and even knowledge mining. A hybrid solution is likely, possibly alternating between mining and hand-tuning to refine the knowledge, but much research and experimentation is needed.

Grid Computing

Grid computing has the potential to enable the sharing of computing power on a global basis, but does not provide users with any new functions per se. Still, the availability of vaster greater computing power could very well enable new and advanced functions, particularly related to knowledge management and machine intelligence. How to effectively exploit that computing power remains an open question for research, but software agent technology is a leading candidate for both enabling access to that computing power as well as using it for consumer-level applications.

Semantic Grid

The semantic grid layers the concepts of the Semantic Web on top of raw grid computing. The massive volumes and vast diversity of computing resources available on a semantic grid literally require software agent technology to find and match the relevant computing resources. Software agent technology also permits the aggregation of semantic grid resources and services to provide higher-level resources and services that enable even higher-level consumer applications.

Centralized Applications are Bad; Distributed Applications are Good

Wide area networks such as the ARPANET and the Internet evolved from a realization that centralized networks have too many problems to scale up to meet the capacity and reliability needs of large-scale computing communities. Although the Internet and Web as networks themselves are decentralized or distributed, far too many applications and services are far too centralized. Each organization wishing to put up an application on the Internet or Web has to explicitly cope with how to scale up their own computing infrastructure as their own computing audience grows. Redundancy, caching, and mirroring are all techniques that have evolved to cope with the difficulties caused by centralization of network applications. All of this highlights the two most important facts of networking: centralized is bad and decentralized or distributed is good. The application corollary is true as well: centralized applications are bad and decentralized or distributed applications are good. Unfortunately, much of the infrastructure and tools we have available to us today are focused on development and deployment of small or centralized applications or semi-decentralized applications in a tedious, expensive, and error-prone manner. So, by focusing on distributed applications we move to a world to eliminates many of the problems that are inherent in decentralized or manually decentralized applications. Put simply, innovators of new consumer applications should not have to waste any of their time, energy, or resources on the problems of scaling and reliability.

Centralized Databases are Bad; Distributed Databases are Good

All of the arguments against centralized applications and for distributed applications apply to databases as well, especially since they tend to be the heart of many applications. So, centralized databases are bad and decentralized or distributed databases are good. Unfortunately, management of distributed data can be even harder than distributed code. Actually, that’s not really true since both are very difficult to manage and we only imagine that we know how to properly manage distributed code.

The important concept for a distributed database is that the various data elements are not under the dictatorial control of a central database administrator. Instead, intelligent software agents monitor and accommodate differences in approach to data modeling throughout the network or web that comprises any consumer application. Further, data is shared among applications and shared among a potentially very large number of applications. Much research is needed in this area.

Network-Centric Applications vs. Global Knowledge-Centric Applications

The current rage is the push for network-centric applications, but that places too much emphasis on the network infrastructure rather than the knowledge itself. Rather, we need global knowledge-centric applications, where the focus is on the deeper and global semantic knowledge itself.

The network that really matters is not the physical network nodes and connections, or even the logical domain names, but the network of consumer-centric knowledge.

Software as a Service (SaaS) vs. Software as Agents

Another current rage is to offer software as a service (SaaS), with a focus on maintaining the core software on more centralized servers rather than on the servers of each customer, and that may or may not make sense for stodgy information technology (IT) shops, but only has limited benefits for consumers. Rather, consumers would benefit more greatly from offering software as agents, where there are no large monolithic applications running on centralized servers, but each consumer has any number of software agents which collaborate with other software agents to pursue goals on behalf of the consumer.

Ant Paradigm

Ant colonies exhibit a significant level of problem solving ability despite the limited capabilities of the individual ants. The ant paradigm has great potential as a model for how software agents can be utilized to collaborate on pursuing significant goals on the behalf of consumers.

Software agents as ants can be deployed for individual consumers or jointly to support collaboration among consumers.

Swarm Intelligence

Related to the ant paradigm, significant research has focused on modeling the structure of software agent systems on swarms of the types found in the biological world for attacking large, complex, and difficult to analyze problems. Even without any centralized control or supervision, swarms frequently exhibit apparently intelligent behavior, called swarm intelligence. The trick is to design the individual agents and their methods of interaction so that desirable swarm behavior occurs. This is too complex for most mere mortals. Once again, software agents are ideal for developing, training, deploying, and monitoring swarms of software agents that are running on behalf of the interests of consumers. Despite the research that has been done, much more research is needed.

Analogies to Plants, Forces, and Chemical Agents

Much of the work on software agent technology has focused on the treatment of agents as if they were animals in an environment. In the biological world we also have plants, forces, and chemical agents. Analogous entities and mechanisms may have great value in the environments populated by software agents. For example, many web services in fact act as if they were plants, producing “crops” which can be “harvested”. Forces may simply be constraints in the computational environment. The analogy to chemical agents in a computational environment are not yet clear, but is worth considering. The bottom line is that we want to assure that the computational environments populated by software agents is rich enough and robust enough to support a software agent ecology that is extremely useful from the perspective of users, namely consumers.

Consumer Knowledge Web

The current Web and the envisioned Semantic Web still maintain centralized application servers and vendors as the focal point of the web, with the users outside looking in. The vision espoused here is of a Consumer Knowledge Web where the focal point is the total knowledge base of all consumers and the consumer-oriented software agents which pursue consumer-driven goals. Vendors are essentially “outside” and looking in.

Consumer Knowledge Web 1.0

It is not clear what capabilities would be available in the initial version of the envisioned Consumer Knowledge Web, call it Consumer Knowledge Web 1.0, but they would evolve over time. It may take a dozen or hundred or even more revisions of the supporting infrastructure to achieve the vision of a knowledge web focused on the consumer.

Consumer Knowledge Web vs. Consumer Web

In contrast to the Consumer Web, which is the portion of the Web which focuses on the interests of consumers, the Consumer Knowledge Web would be the portion of the Semantic Web or Knowledge Web which focuses on the interests of consumers. While the Consumer Web is driven by user navigation, the Consumer Knowledge Web is driven by the activity of software agents acting on behalf of the consumer.

The Consumer Web is based on the presentation of information which has little semantic content (e.g., text, numbers, images), whereas the Consumer Knowledge Web is based on semantically-rich knowledge.

Consumer/Agent Knowledge Web

Maybe the envisioned web should really be called Consumer/Agent Knowledge Web to highlight the centrality of software agent technology to achieving the vision. It is not simply that software agents are utilized in the implementation, but that each consumer will need to conceptualize the Consumer/Agent Knowledge Web as a partnership in which the software agents working on behalf of the consumer are essentially part of the consumer’s mind.

Infrastructure: Toolkits, Frameworks, Middleware, Platforms

It would require tremendous ingenuity, discipline, and effort to hand-code the type of sophisticated consumer software agents that this paper envisions. Instead, it is envisioned that much of that common effort be factored out of each consumer software agent and be embodied in a wide range of agent-oriented toolkits, application frameworks, middleware subsystems, and other platform-related software that collectively provides a very rich infrastructure that supports powerful consumer software agents.

Once in place, the agent-oriented infrastructure will facilitate the rapid development and deployment of consumer software agents with much less effort, but a much higher probability that the agents will operate as expected.

A big part of the infrastructure is the autonomic monitoring capability which detects and automatically recovers from abnormal behavior by agents, and also automatically initiates the execution of logic needed to support declarative software agent capabilities.

Interaction Machines vs. Turing Machines

Traditional software has been based on an algorithm-oriented computing model derived from the computer science concepts related to Turing machines. That was fine for relatively discrete and monolithic software, but doesn’t provide any theoretical support for highly distributed computing. More recent research has focused on interaction machines, with the emphasis on how the black boxes interact rather than what’s in the individual black boxes. Going further, the concept of an agent interaction machine has the promise to support even more highly interactive software systems. More research is needed, and more interaction-based software infrastructure is needed.

Organic Application Development

Applications based on software agent technology can be designed, implemented, deployed, and evolved in a myriad of ways that are either difficult, tedious, or outright impossible for traditional, monolithic applications. In fact, the evolution of software agent-based applications can best be described as organic. Organic application development is based on very flexible interface that are goal-oriented rather than task-oriented.

Mash-Ups as an Application Model

One example of an organic application development model is the concept of a mashup or web services mash-up which relies very heavily on accessing and composing the services of existing applications and Web services.

Autonomic Operation

Although we routinely speak of software agents as operating autonomously, or being autonomous agents, what we really mean is that the user can use the software agent in a “fire and forget” mode, but the existence of the software agent is known to the user. We can also contemplate software agents which are brought into existence by some entity other than the user and that operate without the user’s knowledge. We can refer to this mode of operation as autonomic operation, analogous to the autonomic nervous system in biology. This concept has already taken root to some degree in the form of autonomic computing, although that tends to refer to the underlying operating system and middleware than to higher-level applications.

In essence an autonomic software agent implies indirect agency. User U initiates software agent S which initiates software agent T, implies that T is operating autonomically relative to U. There is still a sense that T is an agent of U, but U may not even be aware of T’s existence.

The benefit of autonomic agents is leveraging, in that the user can gain the benefit of the operation of far more software agents than their conscious mind can deal with.

Asynchronous Operation

While autonomic operation is the desired goal, many consumer goals are greatly facilitated with the much simpler asynchronous operation which means that the consumer and application software can operate independently for a while without direct supervision of the consumer, but the consumer remains aware that an asynchronous operation either remains underway or was at least initiated. With autonomic operation, the consumer is not even aware that an operation is being performed on their behalf. Email servers are an example of asynchronous operation, with consumers able to send and receive email without having to synchronize themselves such as is needed for a normal telephone conversation. A typical email alert is another form of asynchronous operation.

Even simple asynchronous operation is difficult enough to program. We need better tools, better paradigms, better development languages, and better software infrastructure to support asynchronous operation. Even then, autonomic operation is yet another mountain to be climbed.

Radically New View of “A Program” or “An Application”

Today, consumers have no choice but to know about and work with monolithic, large programs or applications. Software agent technology and robust and distributed knowledge infrastructure will change all of that. The vast bulk of code will be distributed and shared so that each user-visible function will be very small and atomic. There will be no need for any consumer to think about concepts like program or application. Actually, the term application will still be relevant, but refer to what the consumer is trying to do, or the domain that the consumer is working in, rather than how the use is implemented. In other words, program and application are implementation artifacts that will no longer be needed by consumers.

Macro Software Agents and Micro Software Agents

A macro software agent is a software agent that works on goals at a level that is of direct interest to a user.

A micro software agent is a software agent that works on a subset of the goals or sub-goals that have been delegated to it by a macro software agent or possibly even by a non-agent computer software application.

Consumers stand to benefit from both forms of software agents. Macro software agents tend to work in terms that the user can comprehend, and can appear to act as assistants for the consumer. Micro software agents enable macro software agents to split the work into pieces that can be delegated in such a way as to take advantage of the inherent parallelism and distributed processing of the Internet, the Web, and the Grid.

Its tempting to think of macro and micro software agents as if they were “big” agents and “little” agents, but size is not the issue. For example, a macro software agent might run within the consumer’s handheld device and delegate to micro software agents which are very large computer programs running on servers or desktop computers. In some cases micro software agents will be rather small in size, but that is not a requirement.

One interesting configuration is that a network of users each has their macro software agents on their handheld devices which delegate goals to micro software agents which then interact which the micro software agents of other users.

Network Effects

A software agent has limited utility by itself, but interacting software agents have much greater utility as the number of interacting software agents rises. This is called network effects. The classic example is a fax machine, whose utility is derived in large part from the population of fax machines with which your fax machine may communicate.

Similarly, a consumer can benefit greatly if their software agents are able to interact with and learn from the software agents of other consumers.

Social Computing

People are already waking up to the potential for new tools to allow consumers to interact in a more “social” manner. Social computing endeavors to provide a social context for our computing activities, centered on users and their interactions. Software agent technology has real potential to help exploit the distributed, massively parallel nature of modern computer networks given the distributed nature of such social interactions.

Human Computing

Human computing focuses on dramatically shifting the balance away from “working with the computer” on its terms, towards the computing working for us on our terms. Software agent technology has the potential of greatly facilitating this shift, primarily by being driven by the evolving knowledgebase that agents will maintain for the consumer. Rather than force the user to deal with the artifacts of traditional computing, software agents will have an increasing ability to comprehend and work with the human artifacts of the consumer. This is more than simply about the user interface, focusing a lot of attention on the knowledgebase of the consumer.

Tribes and Social Values

Even today, ad-hoc groups form on the Internet and Web, but there is minimal support for them overall. Software agent technology can provide the infrastructure support to enable informal groups, called tribes, to come into existence and flourish. Agents can also assist tribes in codifying and promoting group social values. And all of this is possible without the need for the group to invest resources and effort in building the kind of software infrastructure that traditionally would be required for such intensive social interaction.

Dynamic Coalitions

A consumer’s software agents can dynamically seek out other consumers with whom the consumer might have a common cause, such as taking a position on an issue. The collection of consumers who are likeminded can be thought of as a dynamic coalition.

Polls can be taken, not be explicitly surveying consumers, but by querying the software agents that a consumer may have authorized to disclose various levels of information about the consumer’s views.

Dynamic coalitions come into existence and vanish as rapidly as consumers’ views evolve.

A consumer can also indirectly join a coalition, by delegating their own position on an issue or whole categories of issues to some other consumer or authority or organization whom they trust. They can take back that delegation at any time. They can also authorize such a categorical delegation with exceptions, such as where they generally agree with the delegatee, but override selected or sub-categorical positions.

There is no vendor or explicit service needed to initiate a dynamic coalition, but simply the consumer expressing their views and authorizing their software agents to selectively make that knowledge available.

Virtual Communities

Virtual communities exist today, but usually they are server-based. Similar to dynamic coalitions, software agent technology can facilitate and support the formation and prosperity of virtual communities.

As an example, software agents acting on behalf of the consumer can monitor and filter activity in virtual communities and alert the consumer when specified interests are being referenced. The consumer may also authorize software agents to act on their behalf in designated virtual communities.

Mobs and Smart Mobs

There is a natural tendency for groups of agitated individuals to congregate in mobs, potentially resulting in violent or at least disruptive behavior. Further, the advent of personal communications technologies have resulted in the evolution of smart mobs. The Web frequently exhibits similar forms of behavior, especially with blogs or blog mobs. The real challenge is not to eliminate mobs or crowds or even to try to rein them in, but to enable forms of communication and interaction which make it less likely for smart mobs to be vehicles for destructive impact on society, but rather to make them an option for constructive contribution to society. One technical problem is that it is difficult to express a large body of knowledge in a simple conversation or short message. Software agent technology can offer a technical solution by enabling the exchange of significant amounts of knowledge between the software agents which represent the individuals in a smart mob. The software agents for each individual can then alert the individual as to specific bits of knowledge that are most relevant to the situation at hand. The concept is simple, but much research is required to make it practical.

Online Democracy

The politics of democracy and the political process itself is quite tricky. Still, software agent technology can help to mediate and facilitate various aspects of the political process. Much thinking, research, and difficult decisions are needed before online democracy can become a full-blown reality.

Reputation and Trust

Consumers have a critical need to determine whether to trust information and services, or the extent of their trust. Assessment of reputation is part of that process. Consumers are also a source for information about reputation. Software agent technology has a role to play in monitoring, evaluating, and propagating information related to reputation and trust. This is yet another area where significant research is needed.

Ethics and Deontic Logic

Ethics and encouraging acceptable behavior is an important quality of any consumer environment. Deontic logic is an approach to formalizing thinking about “ought” or regulative behavior. Software agent technology has a role to play, whether by playing cop or simply monitoring activities and alerting consumers to suspicious activity. Software agents can also assist groups and communities in formulating and managing their own systems of ethics.

Provenance

Provenance relates to keeping tract of the source and history for knowledge, including facts and assertions. Provenance is useful for both the consumer, either for curiosity or to assess reliability and trust, or for software agents which may make decisions about knowledge based in part on its provenance.

Psychology of the Consumer Knowledge Web

The combination of a vast knowledgebase and the activity of intelligent software agents may lead to the need to consider the psychological aspects of the Consumer Knowledge Web. Whether consumers consider the CKW to be intelligent is one thing, but at a minimum it is likely that the CKW will have at least some psychological impact on consumers. We certainly don’t want consumers to feel overwhelmed by the knowledge or the software agents within the CKW, but how to minimize any negative psychological consequences remains an open research question.

Consumer Attitudes Towards Knowledge

Consumers have a fair amount of experience dealing with traditional information such as text, numbers, images, and media, but few consumers have had any experience interacting with a computer in terms of knowledge. It is difficult to predict how consumers will initially react, or how their attitudes will evolve towards knowledge as a form of information and as a media. Some consumers will relish the thought of teaching or feeding knowledge into the computer, while others may recoil with horror. Much research is needed.

Consumer Attitudes Towards Software Agents

Consumers have a fair amount of experience interacting with the computers as an information appliance, but since few computer applications exhibit much in the way of intelligent behavior, much needs to be learned about how consumers will feel about interacting with the intelligent computational entities that we call software agents. Some consumers will find it a satisfying experience, some will find it uncomfortable, and some may even find it worrisome, belittling, dehumanizing, or even threatening. Much research is needed. The advent of a true knowledge appliancewill be an eye-opening experience for most consumers.

Consumer Attitudes Towards the Consumer Knowledge Web

The combination of a vast knowledgebase and intelligent software agents that are constantly operating within that knowledgebase is a prospect that most consumers have never had to consider, so predicting consumer attitudes towards the combination of the two in the Consumer Knowledge Web is an uncertain proposition. Prototyping of interfaces and simulations of the CKW using real humans on the other side of the interfaces may help, but the sheer complexity of the types of potential interactions precludes full simulation in advance of initial deployment.

On-Demand Knowledge vs. Never-Need-to-Demand Knowledge

On demand is one of the popular mantras for services these days, but a more dramatic approach to empowering consumers in the future is the concept of never need to demand, which is enabled using software agent technology that is always anticipating user needs. Yes, we do need to supporton-demand knowledge, but never-need-to-demand knowledge is what we really want.

Direction and the Journey Itself, Not the Destination

Traditional software is more focused on the destination or end-point of the task and what it takes to get there than on providing richer support for the journey itself. Value-oriented software agents can offer the consumer with more satisfying support oriented towards the open-ended directionthe consumer is interested in exploring.

Persistent Storage of Consumer Knowledge

One of the great lingering technical problems for consumers to where and how to store their data, and the problem only gets far worse as we seek to lean more on digital technology in the years and decades ahead and seek to store information and knowledge that is of ever-higher value.

Storing consumer knowledge on a hard-drive, flash-drive, CD, DVD, remote server, P2P network, etc. is not the answer. New approaches are needed. The P2P network approach shows some hope, but is far too primitive for robust storage of data whose value may span many decades.

A subset of the problem is that a lot of knowledge will reside within the internal state of the many software agents that pursue the needs and interests of each consumer. Mechanisms are needed to give that knowledge persistence.

Even where vendors offer remote servers, there remain issues of geographical diversity, vendor longevity, and simply the preference of consumers to not be locked into a single vendor.

Plenty of research is needed.

I have sketched out a preliminary proposal for a subset of this problem, called a Distributed Virtual Personal Computer or DVPC, but even that proposal falls far short of the full needs for persistent storage of consumer knowledge.

Elimination of Indirect Personal Information

Some information about a consumer may be stored without their knowledge or awareness. Such indirect personal information is common in traditional information systems, as well as the Web and even Web 2.0 (e.g., web cookies), but the goal should be to eliminate all such information. Instead, information about consumers should only be stored in forms that the consumer has complete control over, including software agents that answer only to the interests of the consumer. Rather than directly controlling a consumer’s personal information, the goal is to implicitly provide access to the effects of such information by interacting with the software agents that are under the control of the consumer. And of course the consumer controls who can access even their software agents.

Organizing Consumer Knowledge

Even today, it is enormously difficult for consumers to keep track of all the information on their computers and other digital devices. The magnitude of the problem will only get worse as devices and applications evolve over the coming years. Software agent technology can address this problem since individual software agents are designed to thrive in complex knowledge webs and manage large volumes of information. The consumer stays focused on setting goals, and the software agents focus on seeking out the knowledge needed to meet those goals.

Once we place software agents in charge of managing knowledge, the consumer no longer needs to waste any energy “shuffling virtual paper” to satisfy their needs and interests.

Ontologies, Taxonomies, Tagging, Tagsonomies, and Folksonomies

A simple relational database in insufficient for organizing consumer knowledge. An ontology is a description of all that exists for the domain that it covers. A taxonomy is a hierarchical categorization of the entities of a domain, such as in biology. Tagging is a simple approach by which users themselves associate consumer-defined attribute names with entities that they care about. A tagsonomy or folksonomy is a taxonomy-like organization of entities that is derived from the tagging that is performed by a collection of users. Even modest-size ontologies, taxonomies, tagsonomies, and folksonomies can quickly become far too voluminous and cumbersome for people to comprehend and navigate, let alone use effectively. Software agent technology can use contextual information to provide consumers with personalized views of such categorizations of knowledge. More than simply filtering the data, software agents can interact with the software agents of other consumers and collaboratively work with the structure of consumer knowledge.

Difficulties with Directories and Taxonomies

Taxonomies are actually very complex knowledge structures. They may seem simple, and initial implementations of them have been somewhat simple, they require sophisticated tools and software infrastructure to work well. Implementations such as the Yahoo directory, the Google directory and the Open Directory Project (ODP) work to some extent, but fail for most uses. The extent of that failure is illustrated by the popularity of text search engines such as Google compared to the Yahoo directory. One of the primary cause of the failure is that there are not sufficient tools, especially at the consumer level for setting up and working with taxonomies. The ultimate failure is the fact that taxonomies (and related directories) are not 100% automated. Software agent technology is an approach that can be used to mediate and facilitate interactions between consumers and taxonomies. Consumers need the complexity of knowledge embodied in taxonomies, but are ill-equipped to work with taxonomies directly. That consumers need taxonomies is proved by the popularity of tagging.

Auto-Directory, Auto-Taxonomy

Given the difficulties encountered when human being are assigned the tasks of building directories and taxonomies, it makes much more sense to hand the tasks off to intelligent software, in particular software agent technology, which can constantly monitor the knowledgesphere and contribute to taxonomies and directories as new knowledge becomes available. These auto-directory and auto-taxonomy capabilities can add some very necessary structure to the global knowledgesphere and dramatically simply the tasks of knowledge workers and more fully empower knowledge consumers.

MyLifeBits Lifetime Store

The MyLifeBits Lifetime Store is a research project spearheaded by Gordon Bell at Microsoft that endeavors to store everything about your life. Although focused on media artifacts, it does offer an interesting adjunct to the activities of software agents operating on the behalf of the consumer. And it does address the issue of storing photos, video, and other consumer media.

Knowledge Evolution

Knowledge is no static and evolves over time. There are two tasks here: 1) keeping up with the evolution of knowledge, and 2) participating in the evolution of knowledge. Software agent technology can enable and assist with both.

Consumers themselves can and should be participating in the evolution of knowledge. Software agent technology can both enable and assist the consumer as they evolve knowledge, but software agents can also directly evolve knowledge even without consumer direction.

Knowledge Feeds: Web Feeds and “RSS” for Distributing Knowledge

Knowledge will be generated and modified constantly. Distributing it is a major challenge. One possibility is the concept of a web feed such as is commonly associated with web logging (or weblogging or blogging). Also known as RSS and RSS feeds, but not limited to that specific feed format, web feeds can be used to distribute any type of information, as well as knowledge itself. Specific formats would need to be developed to deeply support knowledge feeds. One problem with the current technology implementation is that the user software must explicitly go through the effort of explicitly reading the web feeds of interest, which is fine for a small number of feeds, but clearly unsuitable when the number of knowledge sources rises into the thousands and even millions. Fortunately, there is no shortage of potential solutions to this issue.

The primary intent here is for a mechanism for communication of knowledge between software agents, but there is also significant potential for communication of knowledge to consumers, as well as an input channel to enable consumers to communicate knowledge to their agents.

Software Agent Feeds

In addition to the use of web feeds for communicating with consumers, they are also an excellent communication model for software agents themselves. In general, the primary output of any software agent might be a web feed or knowledge feed which represents the results of the efforts of that software agent. The use of software agent feeds could dramatically raise the interaction power of the Consumer/Agent Web.

Social User Interfaces

Although software agents operate autonomously most of the time, there is occasionally a need for communication with the consumer. At those times, a social user interface (SUI) is highly desirable, allowing the consumer and agent to communicate in a mode that is convenient, efficient, effective, friendly, and non-intimidating. A SUI would include elements of natural language, speech, gestures, and facial expressions, among other techniques. This remains a research topic.

Scalable: Transcending Scale

Needless to say, technology to support large numbers of interacting consumers needs to be scalable. More than simply the capacity to handle the volume and traffic, the concepts supported by the technology needs to be capable of transcending scale. Software agents, acting on behalf of their respective consumers offer capabilities to operate on very large scales. Consumers need scalable categories for concepts so that they can interact with other consumers who might be working at a different but relevant level of conceptual categorization

Open Source Software

Because of the intensity of trust required on the part of consumers to put their faith in software agents, and a desire to foster and stimulate a robust, vibrant, and innovative community, it would be wise for software agent technology to be as transparent as possible, suggesting that open source software be the rule, although there may be exceptions.

Open Data

As important as it may be for the code of software agent technology to be open source, it is far more important that the data formats used by software agents be open. By adhering to an open data approach, we can greatly facilitate interoperability and network effects.

There are really two distinct elements of open data:

  1. The data formats are readily available and transparent.
  2. The data repositories themselves are accessible.

Facilitate Entertainment

Vast amounts of information are available online on entertainment opportunities. Software agent technology, through its knowledge of the consumer’s interests, can mediate and facilitate the exploitation of entertainment opportunities. In some cases, interaction with other consumers can provide additional entertainment opportunities. Software agents can alert consumers to opportunities that they were unaware of or never even imagined.

Software agent technology can also be used to implement online entertainment capabilities.

Economics

Traditional computer software applications have either been bundled with hardware or a service, licensed for a fee, or subsidized with advertising. This presents a challenge since the bulk of software agent technology runs autonomously and has no user interface to support advertising. Software agent technology also tends to be very fragmented, distributed over many computer systems, and access resources across networks, further complicating any attempts to erect “toll booths”. Finally, the economic value to the consumer will vary widely, so there is no clear method for assessing consumers for “costs” relative to the value that is delivered.

Deployment of software agent technology on a massive scale would clearly place significant load on existing network and computer system infrastructure. That cost must be shouldered somewhere, by somebody.

One technical issue is that the amount of resource usage needed to satisfy a consumer request will tend to be non-obvious, so simply presenting a bill after the fact could potentially be so shocking as to be a complete non-starter.

Challenges

  • How much progress is needed on the AI front to make “intelligent” consumer apps feasible
  • When will we have enough compute power, capacity, and connectively to really exploit the concept of agency
  • Coping and exploiting multiple languages and multiple cultures
  • Building rich enough knowledgebases for software agents to use while respecting personal privacy
  • How to “debug” software agents that are capable of complex, and even emergent, behavior
  • How to convince consumers in general and any particular consumer that a software agent can be trusted
  • How to prevent, detect, and mitigate “rogue” software agents
  • How to enable, support, limit, and manage autonomy
  • Discovering new models for social user interfaces.

What about Microsoft Bob?

If Microsoft Bob had not become a reality and such a commercial flop, people would still be seriously talking about the need for and potential benefits from a Bob-like application with a “social interface”. Clearly, Bob had its faults, but maybe not so clearly Bob also embodied quite a number of valid concepts. We’ve thrown the baby out with the bath water, but maybe we can recover enough fragments of Bob’s DNA to do a thorough analysis of the good and bad and ugliness of Bob so that we can develop a set of principle for going forward.

Yes, Bob was a commercial disaster, but we can do better, much better.

TBD: detail the lessons from Microsoft Bob

TBD: modest proposal for “Next Generation Bob”

What About Ray Kurzweil’s “Singularity”?

Ray Kurzweil is certainly a very bright guy, even there is no reliable metric for judging prognostications about the future. The vision in his new book “The Singularity Is Near : When Humans Transcend Biology” is not incompatible with my thoughts expressed here. Yes, he has a much loftier vision of melding the human brain with artificial intelligence, robotics, nanotechnology, and genetic technology, but none of that would preclude anything I’m suggesting here. His idea of “near” is forty years, and I’m merely hypothesizing about more mundane objectives within the next two to five to ten or maybe twenty years.

Consumer Use of Software Agents for Knowledge-Based Computing < 0.0001% of Kurzweil’s Singularity

However much of a technology advance is required to achieve Kurzweil’s Singularity, I would hypothesize that the use of software agent technology for knowledge-based computing as envisioned in this paper may be less than 1/10,000th of 1% of what Kurzweil’s vision would require. The bottom line is that if if Kurzweil’s vision is wrong or delayed, the vision espoused here is still quite practical.

Computing Models

Although there are many features of modern software which exhibit agent-like characteristics, the sense of agency tends to be constrained by the general form of computing model that is being utilized:

  • Centralized, such as a mainframe or server — the consumer is at the mercy of the “central authority”, as benevolent as that authority might be.
  • Localized, such as a standalone PC — the consumer is too isolated for the software to accomplish much other than simple tasks.
  • Thin, such as a telephone — the consumer can do a lot but must do everything themselves since the thin layer of computing has little capability.

Email is great since it enables asynchronous communications, but it adds negligible intelligence to the communications.

Chat rooms can be fun and offer a social atmosphere, but again offer negligible intelligence to the mix.

Auction systems such as eBay enable a new twist on ancient haggling, but again offer negligible intelligence to the mix.

Shopping “bots” begin to add a little intelligence, but not much.

In all cases the best we’re looking at is large databases, distributed computation, and rapid exchange of information. Those capabilities are great, but the sense of agency and intelligence is still missing.

We have technology for users to collaborate, but they are little better than traditional email and telephone exchanges.

We have technology to distribute raw computing power, but little in the way of distributing knowledge and intelligence.

Procedures vs. Tasks vs. Goals

Traditional computer programs are great for automating discrete tasks or sequences of procedural steps. The real promise of software agents is to move a step higher and automate the pursuit of goals, where the idea is known, but the precise path to fulfill the idea is not known in advance. The agent would have the responsibility of taking a goal and decomposing it and recomposing it as implicit tasks to be performed using resources and services available in the computing environment.

Values and Ideals and Life Goals

A giant leap can be made in the ability of software agents to satisfy the needs and desires of consumers once we begin to support a machine-readable form for the values that a consumer has. That will dramatically simplify the consumer’s task of expressing goals.

We can gain yet another giant leap in leverage for the consumer by empowering them to express their ideals as well.

An even greater leverage for the consumer will come once we have mechanisms for consumers to express their life goals.

Knowledge of a consumer’s goals, values, ideals, and life goals will enable software agents to have a significant level of insight into how a consumer’s needs and desires can be optimally satisfied.

Computing Infrastructure Will Vanish into Transparent Ubiquity

The long-term goal is that the computing infrastructure will vanish into transparent ubiquity, meaning that computer hardware and software will be everywhere and operating automatically so that users don’t even notice its existence, but that’s for the long term. In the interim, the goal is to make computing increasingly more ubiquitous and increasingly more transparent. Software agent technology is a key component of this vision, enabling software to operate on the user’s behalf without needing to be visible to the user.

Ultimate User Interface: Life Itself

As we progress towards transparent ubiquity, the user interface begins to vanish as a computing artifact and begins to blend in with the objects around us. So, we begin to converge towards the ultimate user interface: life itself. By interacting with objects around us we give the underlying software input. That natural input coupled with the vast knowledgebase transparently and implicitly available to our software agents provides the vast bulk of the information needed for software agents to pursue our goals, values, ideals, and life goals.

Retreat into the Background

As we make progress on causing the computing infrastructure to vanish into transparent ubiquity, users will be able to observe that computing functions will begin to retreat into the background. Initially the user will still know that the computing functions are still there, but over time that knowledge will begin to fall away from the user’s consciousness.

Location Awareness

With advances in GPS and wireless networks, computer software within handheld devices can now tailor their behavior to the specific geographic location.

Ubiquitous Computing and Ambient Intelligence

As computing devices become smaller and cheaper and easier to connect, they will become pervasive and embedded in virtually everything around us. This is known as ubiquitous computing. Once the hardware infrastructure for ubiquitous computing is in place, software agent technology can be distributed on that infrastructure and begin to offer services in support of users in such environments. This is known as ambient intelligence, intelligence that is everywhere around us without the need to communicate with computers using old-fashioned user interfaces.

Software agents running in such an environment can tap into both the users in the environment and the knowledgebases for those users, to the extent that each user enables such access.

User Model of Software Agents

Although software agents tend not to have user interfaces and operate “under the covers”, users still need to have some conception of how they view the system and what it is doing on their behalf. Even when we finally do get to the point of transparent ubiquity, the user will still have some conception that objects around them are behaving in somewhat predictable ways. So, even as we seek to further reduce the plethora of conscious computing artifacts, we need to be cognizant of the fact that users are always going to need a user model of software agents. Maybe it is as simple as “my agents” or “the system” or “the Internet” (or “the Agent Net”).

Part of the user model will relate to the process by which agents learn about the consumer’s interests. Part will relate to how instantly the agents accomplish the consumer’s goals. If software agents acting on the behalf of a consumer are taking an extended period of time to accomplish a goal, then consumer will need to be aware that the software agents are “working on it”.

Ultimately, it may simply be old-fashioned human folklore that determines the nature of the user model for software agents, but it would be wise to seed the consumer consciousness with some useful facts.

More Deeply Satisfying than Toy-Like

All too often, someone comes up with an interesting innovation, but the implementation is far too primitive and toy-like to be very deeply satisfying for a broad range of users. The implicit power of software agent technology makes it too easy to produce tools that are by definition too powerful to simply be toy-like. The issue is not that a tool might appear to be toy-like, but that it actually be too shallow and limited to be very useful.

Toys, Games, Fun, and Play

Subject to the admonition to avoid toy-like tools, there is much merit to tools that are as friendly and easy to use as toys and games which engage the user’s desire to have fun while pursuing interests. Activities with significant elements of play to stimulate the user’s interest, motivation, and mental processes are to be highly valued.

Potential Consumer Applications

Sure, we cold come up with quite a long list of potential consumer applications of software agent technology, but the real point is that quite literally every known and conceivable aspect of consumer behavior is a potential target for application of software agent technology, and then some.

One of the potentially more fruitful avenues of pursuit is to use software agent technology to automate autonomic tasks and goals, things that consumer want and need done on their behalf but don’t want to have to consciously consider every moment of every day.

The main point I would make here is that the more interesting consumer agents of software agent technology are those in which each agent is taking advantage of a rich, deep knowledgebase of information about the consumer’s background, beliefs, desires, and intentions, as well as generic knowledge models for consumers in general and various subclasses of consumers. Each software agent that comes along and interacts with the consumer will be able to tap into this knowledge and add to it as well, subject to privacy constraints that are ultimately controlled by the consumer themselves.

Obviously we need robust storage and access control for such knowledgebases so that consumers can feel comfortable that their personal information is both kept confidential and is not at risk of being lost. We need much better storage systems than are presently available for even the most security-conscious organizations.

Potential Killer Apps

At this stage it would be pure speculation to visualize what future consumer-oriented knowledge-based applications will turn out to be killer apps that help consumer-oriented knowledge-based computing really take off. Markets evolve, so the profile of future consumers and their interests has yet to evolve. Besides, the focus of this vision is the platform rather than specific applications. Nonetheless, it is important to contemplate the characteristics that such applications might have since they, rather than the platform nature of this vision, will be what actually draw in real, live consumers.

Facilitate Barter

Software agent technology is more appropriate for facilitating direct consumer-to-consumer (C2C) applications such as barter of goods and services. More than simply directly matching consumers, agents can greatly assist in integrating long lines of chains of demand that may be needed to successfully complete barter transactions where the two originating consumers don’t have a direct matching interest. And all of this without complex, centralized servers.

Consumer GIS (Geographic Information System) Applications

There is nothing terribly new about geographic information systems (GIS), but lately more mapping capabilities have been made available on the Web, including Google. There have even been rudimentary efforts to add some consumer-oriented application features, but to-date the efforts remain quite primitive. What is needed is a much richer infrastructure that is capable of supporting very rich consumer GIS (Geographic Information System) applications. The basic capabilities may seem obvious, but without a rich infrastructure, building of rich applications remains tedious, error-prone, beyond the skills of the average developer or consumer, and frequently outright impossible. Once again, software agent technology can facilitate the development and deployment of rich consumer applications, such as those that integrate the knowledge and interests of multiple consumers.

Architecture of Participation

The heart of efforts to support consumers should be an architecture of participation (a term used by Tim O’Reilly) which empowers consumers to interact and collaborate and organically build their own sense of community. The consumer-centric knowledge web is too complex to be built purely by centralized effort, so it depends on the unlimited growth potential inherent in an architecture of participation.

Consumer-to-Consumer Interactions (C2C)

Many existing agent-like applications for consumers require a centralized server to facilitate interactions among consumers (e.g., auctions in eBay). In contrast, the real power of software agent technology is to enable consumer-to-consumer interactions which enable consumers to directly interact without the need of server-based centralized authorities. In essence, this is a form of Peer-to-Peer (P2P) computing, the difference being that decentralized software agents operate as intermediaries between consumers, under the control and authority of the consumers themselves.

The core concept is the consumers can interact directly, actually indirectly through the software agents that they the consumers control and authorize, rather than requiring some vendor or third-party intermediary who controls the interactions.

In any case, consumer-to-consumer electronic commerce is clearly a fertile domain for application of software agent technology.

Consumer Networking and Social Networking

Social networking has gained a fair amount of popularity, but simply hasn’t gained the traction to be a general consumer phenomenon. Current social networking tools and applications and web sites appeal to certain types of personalities (e.g., the elite, the pundits, the leading edge, the lunatic fringe), but not to the average consumer’s sense of community and socializing.

  • Family networking
  • Organization networking
  • General interest networking (e.g, hobbies, religion, politics)
  • Health issues
  • Demographics
  • Ethnicity

Consumer Collaboration

Consumer networking and social networking merely set the stage for a more powerful category: consumer collaboration, where consumers are not simply communicating, but actually engaging in projects together. Software agent technology can both facilitate such projects, but also instigate and initiate them based on the knowledge and interests of the consumers that is available to their software agents.

Software agents can both sift through the vast amounts of networked knowledge to find information of interest to the consumer, and can actually reach out and make contact with the software agents of other consumers who might have a common interest.

Software agents can simultaneously pursue the interests of the consumer, while protecting the privacy of all consumers. By protecting consumer privacy, consumers can feel more confident in giving their software agents freer reign and a wider reach.

Implicit Collaboration

The concept of active software agents enables a consumer’s software agents to constantly be seeking out and possibly even pursuing collaboration opportunities that the consumer may not yet be consciously aware of. The potential for such implicit collaboration boggles the mind.

Yes, the consumer still maintains control over the extent to which such opportunities might be pursued, but the consumer is freed from needing to do all the dog work to uncover the opportunities.

Consumer Intelligence

Analogous to the concept of business intelligence (BI), which aims to work with knowledge about the processes within a business, consumer intelligence (CI) aims to allow the consumer to work with knowledge about their own lives.

Life Mentor

One of the most obvious and richest applications of software agent technology is to have software agents which have been programmed with knowledge about your career and life plans and can offer guidance along the way. The life mentors can offer advice and assistance with the many forms of planning that occur in our lives, including nutrition, health, education, housing, financial affairs, career, family, etc.

Life Agents

The concepts of life mentor and life agent are closely related, but the key difference is that a life mentor is more of an assistant that gives you feedback and suggestions and advice, but life agents can also simply do useful things for you that you may not even know or care about.

Put a different way, a life mentor would address tough, growth-oriented conscious decisions, whereas a life agent can also address subconscious details of the consumer’s life.

Lifelong Learning

Software agent technology enables a richer and deeper semantic modeling for the learning process which can provide a more robust level of support for consumers as they transition through the many stages of learning throughout their lives. Lifelong learning will become a concept recognized by software agent applications rather than a concept that is exterior to the world of computer software.

Role in Education

The role of the Consumer/Agent Web in traditional education is an open question. Certainly software agent technology can be of great assistance, but traditional education is such an emotionally and politically-charged area, that much more careful thought is needed.

If given the opportunity, software agents can assist individuals in learning by keying off the students existing knowledgebase, especially when coaching and mentoring might be needed.

Software agents could greatly facilitate cooperation, collaboration, and project-oriented work by groups of students.

Empowerment

Software agent technology can be used to generally assist in empowering consumers, helping them to identify opportunities for pursuing their interests.

Facilitate Leadership

One important way that software agents can assist consumers is to facilitate leadership. Rather than being merely passive consumers or even pursuing a modest degree of activity, consumers can be empowered to take on leadership roles. Software agents can help to identify opportunities for leadership and facilitate consumers being able to exploit such opportunities. Software agents can assist consumers in gaining access to the knowledge needed to pursue leadership opportunities.

Facilitate Creativity

By its very open-ended nature, software agent technology is inherently oriented towards supporting creativity. By comprehending the consumer’s interests and having access to the vast networked knowledgebases, including those of other consumers, software agents are uniquely positioned to offer support and suggestions for the consumer’s creative pursuits.

Facilitate Imagination

Beyond support for creativity, software agent technology with knowledge of the consumer’s interests and behavior can support that special portion of the consumer’s mind known as their imagination, the source and driver for their creativity.

By providing support for organizing ideas, thoughts, and images, software agents can become an adjunct to the consumer’s own imagination.

Going beyond mere organization, software agents can take a more active role and retrieve information from knowledgebases, interact with the agents of other consumers, and even facilitate the direct interaction of consumers, when appropriate and enabled by the consumers themselves, to enable them to enhance each other’s imagination.

Facilitate Dreaming and Aspiring

Everybody has dreams (the conscious kind) and aspirations, but pursuing them and achieving them is another matter. Software agent technology can help. First, consumer applications are needed to assist the consumer with expressing their thought about their dreams, hopes, and aspirations. With that knowledge in the consumer’s personal knowledgebase, software agents can then seek out global knowledge and interact with software agents representing other consumers and even mentors to exploit knowledge that can be shared. Of course the consumer’s privacy will be completely respected, but the software agents working on the consumer’s behalf can alert the consumer to resources and contacts that can help the consumer pursue their dreams and aspirations. Software agents can also help if the consumer is unsure of their dreams and aspirations and seeks information, advice, coaching, and mentoring. Not that the software agents can necessarily act in that capacity themselves, but the global knowledgebase and global web of software agents representing other consumers is a vast resource that can be tapped. The precise modalities of support for dreaming and aspiring are far from clear, but what is clear is that it is an area which is deserving of significant research.

Facilitate Natural Language Interfaces

Natural language interfaces are notoriously tricky and extremely dependent on domain, context, and the users. The knowledgebases maintained by software agents contain a wealth of domain, context, and user knowledge which has the potential to provide a rich enough level of guidance to natural language interface software so that realistic natural language interfaces become much more practical.

Overcome Language Barriers

By maintaining as much knowledge as possible in a language-neutral semantic format, consumers will be able to access a vast amount of knowledge that would not otherwise be easily accessible if it were stored as raw natural language text. Software agent technology can be used to facilitate the origination, translation, and management of knowledge in both semantic and natural language formats.

By enabling consumers to communicate in higher-level semantics, consumers which read and write and speak dissimilar natural languages will in fact be able to communicate, at least to some degree.

Opt-In is the Law

As a general rule, the relationship between consumers and vendors is most fruitful with an opt-in approach to communication and commitments. Consumers will benefit greatly by knowing that they are always being treated fairly by vendors.

Software agent technology does provide an interesting twist since the consumer has the ability to delegate some degree of opt-in authority to their own software agents. But, the key is that the consumer has that control and would need to opt-in to delegate any of that control. The consumer will also have the authority to rescind any of that delegated authority at any time and for any reason.

Early Adopters: High-end (Professionals) and Low-end (Kids)

There are plenty of consumer applications that could benefit greatly from the use of software agent technology, but we need to focus first on who is more likely to use these new technologies and applications and trust that interest will then gradually filter out into the broader demographic base (e.g., your average “dumb user”), and it would appear that both ends of the demographic spectrum would be more likely to quickly adopt the new technology and applications than the middle of the demographic curve. The high-end demographic is likely to be professionals who keenly sense high value from a focused use of the technology. The lower-end demographic is likely to be kids (say 15 to 25 years of age) who find the new possibilities of the technology and applications to be exciting, cool, challenging, and a great way to rebel against the odd, stodgy, entrenched traditional applications.

Professionals will appreciate the ways that software agent technology can adapt and be adapted to suit their specific needs, while kids will appreciate the creativity that software agent technology offers them.

Handheld Applications and Mobile Applications

Given the severely limited graphical user interface of handheld devices, including mobile phones, software agents would seem like a natural technology for assisting users of such devices.

Traditional user interfaces and even high-end graphical user interfaces are more procedure and task-oriented, so anything that shifts the balance towards the goal-oriented end of the spectrum has the potential of dramatically lightening the user interface burden for handheld devices.

Many common goals could be pre-programmed into the handheld or server software. Then, in conjunction with a knowledgebase about the user and context of the physical handheld device (e.g., physical location and accessible local devices), a far richer level of defaults can be made available to the user.

Mobile Environments

Mobile environments such as cell-phones, handheld-devices, and motor vehicles present a whole new level of application considerations that were not an issue for fixed computers. Once again, the added complexity is a great match for software agent technology. Software agents can be readily applied to every consideration that arises in mobile environments.

Mobile-Agent Applications

There are three forms of mobile agent applications: 1) applications than run in mobile, handheld devices, 2) applications that run on servers in support of mobile devices, and 3) applications composed of migratory software agents that are able move or be moved between computer systems, including mobile and handheld devices. In all three cases, software agents can perform significant functions on behalf of the consumer, with a higher degree of robustness, scalability, flexibility, and user-friendliness.

The important common feature is that there will no longer be a one-to-one correspondence between a hardware device and the software that runs on it. Hardware will be distributed (e.g., mobile devices and accessible servers and ambient computing hardware) as will software (modular components and software agents), and the two will be combined in a dynamic manner as mobile devices move around.

Management of Consumer Data

As personal computers and other personal electronic devices begin to take on a larger and central role in the lives of consumers, the management of the consumer’s data becomes a larger and larger problem. This is yet another opportunity for software agent technology. Software agents can transparently assure that data is stored in a secure location and is readily available when it is needed and where it is needed.

DVPC: Distributed Virtual Personal Computer

Local storage in an electronic device such as a personal computer is convenient, but has some drawbacks. People struggle continuously with the issue of who to best “back up” their data, not to mention where to store backups and then how to access them. People also struggle with how to recover from mistakes and mangled data and recover from such problems. The distributed virtual personal computer (DVPC) concept is designed to avoid all of these problems. First, the local storage is only a cache or copy of the “real” data, which would be stored on multiple, network-accessible storage systems (not simply one central server). Second, “smart versioning” will allow the user to navigate through all changes in the history of a file so that no data is ever lost. DVPC would automatically propagate changes to the consumer’s data to all computers which have been designated to be part of the consumer’s virtual personal computer.

DVPC would also enable the consumer to selectively make data sharable by other consumers and software agents. DVPC would be an ideal repository for a consumer’s software agents to store and access data that belongs to the consumer.

At present, DVPC is only a concept, with no plans in place for its implementation.

Virtual Networked Bits

The concept of virtual networked bits addresses the issue of having a robust method for storing user data that does not rely on the reliability of a local or master copy of your data or even manually storing copies elsewhere. The intent here would be that all consumer knowledge would by definition be stored as virtual networked bits so that consumers never need to worry about lose of their information and knowledge.

Sharing of Consumer Data, Information, and Knowledge

Sharing of consumer data, information, and knowledge is quite problematic with today’s computers and networks. Specialized services continue to spring up like weeds to facilitate selective sharing of data, including media such as photos, audio (e.g., podcasts), and videos, but the extent of the underlying problems is amply illustrated by the never-ending emergence of new services. On the other end of the spectrum, there seems to be a never-ending stream of horror stories relating to identity theft, hacking, viruses, etc. demonstrating that keeping information private is as problematic as sharing it. A core issue is that it is at present too difficult for consumers to simply manage their information at all. This suggests the need for the knowledge-based software agent technology that can assist the consumer in managing their information, including the decisions about which information should be kept private, which information should be available to the world, and which information should be available to selected groups of consumers. Software agents can then assist in the dissemination of information to those to whom access is granted.

Consumer-Centric File System — Consumer-Centric Knowledge Organizer

All of the problems with consumers managing their information point in the direction of a need for a radically different form on file system, a consumer-centric file system, one that may bear no resemblance to the computer file systems of today. More than just a system for organizing computer files, we really need a consumer-centric knowledge organizer, one that comes with an army of automated librarians, implemented using software agent technology, to automatically collect, organize, disseminate, and access the wide range of knowledge that confronts consumers throughout their lives.

Management of Medical Records

Management of medical records remains an unsolved problem. Software agents in conjunction with distributed management of consumer data present an opportunity to both manage medical records better and to give the consumer more control.

Existing, proprietary approaches to automating and managing medical records simply don’t have the critical mass to achieve success, and don’t even come close to letting the consumer participate in the process.

Health, Nutrition, and Medical Applications

Much research is needed into how computers and computer networks can be exploited to aid consumers in their health, nutritional, and medical needs. Software agent technology can mediate and facilitate consumer access to information and services. And in some cases, software agents can directly provide services, such as nutritional monitoring. Software agents can also mediate and facilitate interaction with other consumers, such as sharing experiences and support groups.

Legal Applications, Management of Legal Information

Although it’s too big a leap to suggest that software agents might offer legal advice and eliminate the need for lawyers, there is still a lot of information about a consumer that can be managed more effectively by software agents. Software agents can also monitor the consumer’s activity and advise them if there are any situations that might suggest a need for legal advise. This would all be under the control of the consumer. There would be no Big Brother watching over them. Software agents can also keep track of information about consumer transactions and interactions which might be of value in any future consultations with lawyers. And finally, software agents can be used to keep track of past legal proceedings and discussions for future use. Software agents can also track the consumer’s current legal situation and make discrete inquiries of other consumers about their experiences in similar scenarios. Since personal details are kept completely private, consumers can effectively have safe conversations about sensitive legal matters with other consumers, knowing that their personal details are explicitly kept out of the discussions by the mediation of software agents.

Coping With Uncertainty

Everybody encounters uncertainty in their lives on a frequent basis. Coping with that uncertainty is an ongoing problem and even paralyzing for some people. Software agent technology can offer consumers assistance with uncertainty, helping them organize their thoughts and consider options and choices. Agents can access common knowledgbases for information relating to decisions where uncertainty is an issue. Agents can make inquiries as to how other consumers with similar profiles have handled similar uncertainty. Agents can hook up the consumer with others trying to cope with the same or similar uncertainty. Finally, software agents can arrange for human mentoring related to the uncertainty.

In any case, keeping a detailed profile of the consumer enables the software agent to have a much more “intelligent” starting point for assisting the user.

Facilitating Roles and Personae

Every person plays a number of roles in their life and may also have any number of personae that they express and are known by others. Software agent technology can facilitate the complex and confusing information, knowledge, and interactions that come with playing multiple roles and having multiple personae.

Learning to Learn

Computer-aided instruction (CAI) has been around for many years (decades), including the current popularity of eLearning, but much of this so-called “learning” is really training. Learning is a much more difficult proposition. In particular, we have the problem of learning how to learn. Once again, software agent technology can be applied. The goal here is not to train the consumer a bundle of pre-programmed knowledge, but to give them tools and support that empower them to actually learn on their own, especially in new and unexpected environments. Agents can help by having access to the consumer’s profile and history, consulting generic knowedgebases, searching for other users who have had to cope with similar learning situations, and possibly even invoking the aid of a human mentor.

Auto-Search, Intelligent Search Alerts and Notification

Even the most powerful search engines today are still fairly primitive. Much research is needed to advance the state of the art.

Auto-search means that software agents are continuously monitoring the consumer’s interests and activities and automatically initiating search queries to collect information and then organize it in ways that align with the consumer’s interests and activities. The goal is simply to give the consumer the knowledge they need, when they need it.

A variety of intelligent search alerts and notification schemes are available today, but in rather primitive forms. In truth, they simply don’t work very well even when the consumer takes the trouble to learn how to use the tools. Software agents can be deployed to handle all of the bookkeeping, in accordance with auto-search to provide useful and user-friendly alerts and notifications.

Semantic Search: Deep Context vs. Simple Keywords

Today’s search engines focus primarily of searching based on simple keywords, but are clueless about the meaning of those keywords. Knowledge-based software agent technology can exploit the consumer’s knowledgebase and context to do a true semantic search based on meaning rather than textual keyword matching.

Go to Google vs. My Agents are on It

Although Google and other search engines do have the concept of a search alert, it’s rather simple-minded. Going far beyond a simple keyword orientation, software agent technology can support goal-oriented auto-search, which attempts to determine whether newly available information aids in meeting the goals of a consumer rather than merely matching some keywords. So, instead of going to Google to explicitly get information, the consumer can simply sit back as “my agents are on it.

Searching vs. Sleuthing

The difficulty with existing, and even proposed search engine capabilities is that it’s still a simple search and depends on the consumer to initiate and pursue the refinement process. Instead, we need sleuthing, where the consumer simply supplies a few clues and intelligent software agents do the heavy-lifting of sleuthing for answers, including reasoning based on real semantics of both the query and the data. Part of this will depend on sophisticated semantic webs, ontologies, and taxonomies, part depends on histories of similar searches (or sleuths), part depends on interacting with the software agents of other consumers. It is a hard problem, and worthy of significant research, but would be well worth the effort.

GMWIMW — Give Me What I Might Want

With all the talk about search engines, personalization, tracking, histories, etc., there is a little too much focus on trying to give the user results that their past history suggests that they would want. Maybe it’s just me, but I have a different interest than merely wanting to see stuff similar or related to what I’ve seen in the past or what people similar to me are interested in. I’m always searching for new stuff, so what I would most like the computer to do is to “Give Me What I Might Want” or GMWIMW.

This is actually the opposite of using my past history to predict what I might be interested in. Rather than take my history and moving delta to similar topics that correlate well with my past interests (or even new results of people similar to me), I want to make a quantum leap in some unexpected direction and get results that will likely have the lowest possible correlation with my past interests (or the results selected by people similar to me).

This is what I want the computer to do. Whether this is feasible, is another matter.

Actually, I do know for sure one technique that at least offers the possibility of showing me results that I might want: randomly select an item of information that I’ve never seen before. Now of course that will frequently (usually) give me all sorts of uninteresting stuff that I have absolutely no interest in. That’s okay. Just give me a little button so that I can signal topics that should be semi-permanently crossed off my potential interest list. I say semi-permanently, because even then, the computer might periodically query me as to whether some of those topics should really stay on my “do not show” list. It could do this by displaying closely related results (to the results I’ve expressed an extreme disinterest in) on the off chance that there was simply some superficial detail that discouraged me. In any case, after a short while, the computer would have quite an impressive library of topics and sub-topics that can be weeded out of even a random GMWIMW process.

I’m not suggesting that GMWIMW should be a random process, but at least there is some hope that GMWIMW could conceivably be implemented.

To me, this is a “growth-oriented” search strategy. One that seeks new paths. One that seeks new horizons. One that seeks enlightenment. One that seeks inspiration. One that seeks innovation. One that almost makes the computer seem to have something like intuition.

On the other hand, I don’t presume for one moment that my interests in GMWIMW coincide with those of the average search user.

Still, almost everyone has moments when all the traditional, methodical, and even heuristic strategies and techniques for making incremental forward progress are not getting you anywhere. Those are precisely the times when GMWIMW is the optimal search strategy.

Thinking Outside the Box

People are instructed to think outside the box, but that’s much easier said than done. Software agent technology can help in the sense that a rich context of software agents around the consumer can provide a clear indication of where the box really is, and then the agents can offer the discipline to seek out knowledge and opportunities that really are outside of the consumer’s current “box”. Software agents can offer the appropriate support for the consumer, whether to hold their hand through the process or to give them a not-so-gentle push to get out of the box. A very wide range of customizable support can be offered.

Out of the Blue

Software agent technology can also offer consumers “out of the blue” experiences when they wish to “get out of the rut”. The rich knowledge context for the consumer, coupled with the ability to exchange information with the software agents for other consumers as well as the knowledgebase of global experiences enables software agents to suggest and even pursue experiences that can be “out of the blue” for the consumer.

Peer-to-Peer (P2P), Agent-to-Agent (A2A) Metaphor

Peer-to-peer (P2P) networking, as popularized by P2P file sharing is a useful computing metaphor, but is made far-more powerful when it is intelligent agents that are communicating and exchanging information. Agent-to-agent (A2A) interaction is a very powerful computing metaphor and dramatically reduces the level of consumer interaction required to achieve a consumer’s goals.

The agent-to-agent metaphor requires a much more sophisticated level of infrastructure support, but is also capable of delivering a much higher level of intelligent support for both the interaction of consumers and the pursuit of consumer goals.

Robots

Consumer-oriented robots are a great opportunity for introducing software agent technology to the consumer market. To date, low-end robots have been quite primitive and hardly better than toys, but the potential is certainly there.

Mass Customization

Mass customization is a business strategy that aims at producing goods and services for the needs of individual consumers, while achieving economics of scale in operations. Personalization is but one aspect of this customization. Software agent technology is the best-positioned technology to pursue both personalization and customization of services to meet the needs, goals, and desires for producers, distributors, and consumers of services.

Blogging and the Blogosphere

Blogging is a fairly recent phenomenon, but shows a lot of promise for interaction among consumers. Unfortunately, blogging is a bit too tedious and uncomfortable for many people. Once again software agent technology can come to the rescue. Software agents can be pre-programmed with a deep enough knowledge of the blogosphere and the consumers knowledge base to greatly facilitate the consumers experience with blogging.

Blogs are a fairly primitive, but semi-structured form of knowledge. Software agents can help to link the information in blogs back to the more structured consumer knowledge base.

Auto-Blogging

Many blogging events are in fact fairly predictable and driven by the nature of the consumer’s behavior patterns. Rather than the consumer needing to manually take the step to create a new blog post, software agent technology can be applied to automatically perform blog posts on behalf of the consumer. Such auto-blogging can dramatically simplify the consumer’s online life. In some cases the consumer may wish to have full control, but other times it may be simpler, more convenient, and more comfortable for the consumer to put the auto-blogger agents on auto-pilot. In any case, the consumer is always in control.

Mobile-Phone Applications

Mobile-phone applications are an excellent area for the use of software agent technology. Given that the consumer has a limited user interface and attention span, it makes perfect sense to have network-based software agents which are off pursuing goals for the consumer, especially while the consumer is not connected.

Fuzzy Logic

Consumers have great difficulty being precise and specific in expressing their needs. Traditional computer software has worked well to the extent that users provide precise input. Fuzzy logic is a concept from philosophy and artificial intelligence that explicitly addresses the inherent difficulties of insisting on precise specifications. Software agents have a great opportunity here to introduce the concept of fizzy logic into the mainstream so that consumers can focus on expressing what they know, regardless of how imprecise their knowledge may be. Many applications can work best when organized as journeys of discovery rather than starting with a presumption of a single, direct path.

Put simply: if a piece of computer software does not support fuzzy logic, then it’s not likely to be an intelligent software agent.

Constraint Management to Automate Functions

In traditional software each application needs to explicitly access any information which may have changed. An alternative is what is called constraint management, which allows applications that use information to declare their needs and then an intelligent infrastructure registers those needs so that the application will be automatically alerted when any of the needed information changes.

It can be very tedious and error-prone for applications to keep up with changing information. And that’s for information sources that are known in advance in detail to the application developers. Constraint management can automate that process.

In addition, an application can register its interests in whole classes of information so that new streams of information can be readily accessed as they come into existence. Constraint management can empower application developers to focus on the functions they wish to perform, while the infrastructure takes care of managing information streams and automatically invokes application functions as declared by the developer.

Psychology Applied to Software Agents

As the interacting communities of software agents become larger in size and the interactions more complex and competitive, we will need to consider the psychological aspects of agent interactions. Software agents will need strategies for coping with complex social interactions, and will need to consider the social aspects of interacting with consumers themselves. And, software agents will need to consider the psychological impacts of their actions on the consumers for whom interacting agents are acting. Lots of fertile research ground here.

Spam and Irrelevant Knowledge and Useless Information

Knowledge and knowledge flows are just as susceptible to spam as is traditional email. Software agents can of course mediate and reduce the flow of knowledge spam. In addition to outright spam (e.g., unwanted commercial messages), users can also be bombarded with legitimate knowledge that merely happens to to either outright useless to the user or irrelevant to the task and goals at hand. Software agents, with their knowledge of the needs and interests of the consumer can once again mediate to assure a useful flow of knowledge.

Identity and Anonymity

Deep knowledge of the consumer won’t be permissible until we have a rich enough identity meta-model which will robustly prevent fraud and other mischief related to attempts my malicious parties to misrepresent their identities. On the other hand, software agents need to cope with consumers who wish to protect this anonymity.

We need a rich identity infrastructure, not as a monolithic, centralized system, but as a distributed system that protects all consumers as well as all vendors.

We need rich selective disclosure mechanisms so that applications can gain access to information needed to optimize personalization of services, but also that limits access so that privacy and anonymity are also protected.

Consumers need repositories or “banks” for their personal information, places where the information can be protected by third-parties that have no vested interest in applications that the consumer may wish to interact with. Consumers can then authorize their chosen “identity banks” to disclose only as much of their information as they want disclosed and only to those parties that they authorize. The identity bank also provides a mechanism for vendors to verify or access personal information as needed and as authorized by the consumer.

Having a rich identity mechanism is essential to this process.

Digital Identity

The validity of a digital identity does not guarantee that this electronic identity really does match up with a specific real-world identity. Synchronizing the online digital world and the offline real world is an unsolved problem

Identity Theft

Identity theft has certainly gotten a lot of publicity and much work has been done to mitigate it, but it remains an unsolved problem.

There really are four discrete problems: 1) Real-world identity theft within the real world, 2) Online digital identity theft within the online digital world, and 3) Misuse of a real-world identity in the online digital world, and 4) Misuse of an online digital identity in the real world. Any particular solution may address one or more of the four problems, but a successful solution to one problem does not guarantee a successful solution to the other three problems.

Identity Union

One approach to managing the personal information about a consumer that relates to their identity is the concept of an identity union. Previously, I’ve written about a related concept called a Data Union, which is essentially a “bank” where consumers can voluntarily “deposit” personal information that can then be selectively provided to vendors and other consumers with a high level of confidence on the part of all parties. The word “union” is used here in the sense of a consumer “credit union”, a place where consumers feel comfortable placing and discussing their financial affairs.

So, the concept of an identity union is that the consumer can place any amount of personal information “on deposit” at one of more “identity unions” of their own choice (or subject to criteria of their own choice), and then the consumer and their agents (e.g., software agents) can grant access to selective amounts of information to vendors and other consumers as they see fit, with full confidence that nobody will be given information which they are not authorized by the consumer (or their agents) to receive.

Identity details can include real-world information about the individual, including photos, fingerprints, blood type, DNA details, etc.

An identity union would ideally have a real-world location where consumer information can be verified by people and equipment as opposed to being whatever anybody might upload on a public network.

An identity union would have a reputation, auditing procedures, training protocols, etc. so that both the consumer and authorized users of the identity union can have very high confidence in the validity of the consumer’s identity.

Privacy

Privacy is an ongoing struggle.

Although software agents need even more details about our personal lives, the real opportunity is that by shifting personal information into agents, we have a better chance of minimizing the amount of personal information that is needed or captured by businesses and governmental entities.

Much work is needed in this area.

Security

Security is and will always be a problem, but more so as we broaden the scope of applications, broaden the audience of users, and add such wide-ranging infrastructure that there are an astronomical number of points of potential vulnerability. Much research is needed, but software agent technology can be of great assistance, both in monitoring and enforcing security constraints, and facilitating interactions in a way that leads to severely-narrowed opportunities for security breeches.

Big Brother

With visions of Big Brother from George Orwell’s 1984, it will be essential to craft a computing infrastructure which minimizes the likelihood that an intrusive government would get any unnecessary access to the personal information of consumers. Decentralized computing as epitomized by autonomous software agent technology is a very appealing approach to deter Big Brother.

Law Enforcement

As much as we would like consumers to have absolute control of their lives and their data, there are legitimate law enforcement interests that may require gaining access to consumer data. How to do that in a way that doesn’t give law enforcement authorities total, unfettered access is an open research question, but distributed, autonomous software agent technology coupled with robust access control mechanisms would seem to be an appropriate approach to pursue.

Terrorism

Terrorists will always seek to exploit technology which enables them to communicate in ways that are less-likely to be detected by law enforcement authorities. Nonetheless, it will be important to have sufficiently robust safeguard mechanisms so that terrorist activities can be detected and reported to the appropriate authorities. Software agents can at a minimum provide a robust monitoring mechanism.

Information Warfare

Information infrastructure, both hardware and software, is a plausible target at times of war, including terrorist attacks. Therefore, it is critical that our computing infrastructure be robust enough to deter and mitigate any negative consequences of information warfare. Software agent technology can play a role, including monitoring and intervention. Further, the distributed nature of software agent technology tends to assure that applications, services, and data are less susceptible to attack, or at least that consequences are less likely to spread.

The flip side is that software agents could be utilized to engage in offensive information warfare. The good news is that the level of infrastructure needed to support advanced software agent technology will inherently make it likely that safeguard checks will detect attempted information warfare attacks.

Nonetheless, much research is needed in this area.

Levels of Autonomy

Autonomy is an extremely important quality for software agents, but it presents many difficulties and can be quite dangerous (like fire) unless managed properly.

I have identified a number of levels of autonomy:

  1. Level 8 — independent entities which do not interact under any circumstances
  2. Level 7 — independent entities which sometimes interact of their own volition
  3. Level 6 — independent entities which sometimes interact out of an enforceable obligation or contract
  4. Level 5 — performance of specific, well-defined tasks in a synchronous manner for the user. Traditional computer software
  5. Level 4 — limited to delegation of specific, well-defined tasks that can be performed asynchronously for the user
  6. Level 3 — side-by-side, semi-supervised asynchronous operation in pursuit of one or more goals
  7. Level 2 — independent operation in pursuit of goals, as initiated by the user
  8. Level 1 — independent initiation of operations in pursuit of goals
  9. Level 0 — covert or autonomic operation in pursuit of goals believed to be of value to the user, but without seeking the direct advice or approval of or even notifying the user directly

Levels 6 through 8 are forms of autonomy not normally associated with agency.

Levels 4 and 5 are primitive forms of autonomy associated with agency.

Levels 1, 2, and 3 are the general target for the application of software agent technology.

Level 0 in fact may have the highest potential value, but is also the riskiest and most difficult to achieve.

Social Structures

Consumer-oriented software agents will need significant awareness of the social fabric of which the consumer is a part, including:

  • Organizations
  • Citizenship (local, regional, national)
  • Family
  • Business
  • Affiliations
  • Demographics

Knowledge of the consumers relationships can dramatically enhance a software agent’s ability to support the consumer

Consumer-Centric Tools

Existing knowledge management tools are oriented towards professionals, rather than the needs of consumers. A significant level of skill, aptitude, training, and patience is needed to engineer knowledge in existing systems. Even then, the encoded knowledge is not up to the level of depth envisioned here. Beyond all of that, one key distinction is that consumers are not working on behalf of some organization which dictates a framework, but have their own open-ended interests at heart. Tools for the consumer-centric knowledge web must be consumer-centric and recognize that the user of the tools is the focus of the knowledge to be managed. The tools need to take into account the fact that the consumer lacks a feel for the underlying difficulties of knowledge management. More importantly, the tools need to be built based on the understanding that the user, the consumer is not merely managing knowledge, but in fact is frequently creating new knowledge, that may not even fit into any existing structure. Lots of research needed here.

Never a Need to Agree to “Trust Us”

The consumer-centric model espoused in this paper dictates that consumers never are required to accede to the demands of any entity that the consumer “trust us.” Rather, the consumer and their software agents will always be in a position to say no to requests for trust and always be free to take steps to validate the trustworthiness of any entity before agreeing to interact with that entity. Key to ensuring that no consumer is ever placed in a position where trust is forced, the knowledge infrastructure of the consumer-centric knowledge web must be distributed in such a way that no vendors are in a position to acts as “trust us” gatekeepers.

Human-Like Interface

A lot of the thinking about software agent interfaces has focused on trying to make the interface human-like, such as synthetic characters. Although this approach makes sense in a lot of cases, the primary focus should be on eliminating the human-agent interface entirely and using an implicit interface or an inferred interface, where the software agents are interfacing with the knowledgebase of the consumer rather than the consumer themselves.

Even in cases where a software agent does need to communicate directly with a consumer, the interface should be one that makes sense and works effectively, regardless of whether it is human-like or not. For example, you might engage a software travel agent in an email conversation (much like the one I had with my real travel agent to weeks ago).

I am not arguing that consumers should be confronted with computer-like interfaces at all times, but simply that we should constantly be looking for interfaces that transcend both traditional computer and human interfaces, where it makes sense.

The really important concept is that software agents communicate in a rich but abstract messaging format that can be translated by a user interface layer into the preferred form of communication for the individual consumer.

Intellectual Property: Enabler and Obstacle

The legal mine field of software patents is immensely significant to the emerging field of software agent technology.

Sometimes, patents are used to attempt to control a sub-sector of the economy and to preclude new entrants.

Other times, the economic power of patents can act as an economic incentive to spur innovation and investment in an area.

One of the keys is to seek to evolve the relevant markets in such a way that patents tend to apply to infrastructure vendors who can readily afford to license patents, but that application developers can freely innovate and develop applications without the burden of worrying about patent licensing or potential infringement. Essentially, we need to have open, “free enterprise” zones with regard to intellectual property so that innovation and business development can occur at a healthy and rapid pace.

Legal Aspects

If a computational entity such as a software agent truly is given a sense of agency related a legal entity, such as a person or real-world organization, then in theory that software agent would become an entity of interest to the law, governments, and the courts.

Architecture

There will be many different possibilities for specific architectures for consumer applications that use software agent technology, but here are some of the elements that are likely to be of high value:

TBD

Software Agents in Fiction

As usual with science and reality, representations of theoretical science tend to pop up in science fiction before the science becomes a reality. This has already been proven to be true with software agent technology.

  1. Robby the Robot in Forbidden Planet, 1956.
  2. The Robot in Lost in Space, 1965.
  3. Colossus and Guardian, two artificially-intelligent supercomputers with minds of their own in Colossus by D. F. Jones, 1966, and in the movie Colossus — The Forbin Project directed by Joseph Sargent, 1970. The ironic thing is that once the computers developed minds of their own and began pursuing their own agendas, they were then so longer acting in the capacity of agents.
  4. HAL, the self-aware computer, in 2001: A Space Odyssey by Arthur C. Clarke, 1968.
  5. Dixie Flatline in Neuromancer by William Gibson, 1984.
  6. Colin, the personal assistant, in Mona Lisa Overdrive by William Gibson, 1988.
  7. The Librarian in Snow Crash by Neal Stephenson, 1992.
  8. Agent Smith, et al in The Matrix movie series, The Matrix, 1999, The Matrix Reloaded, 2003, and The Matrix Revolutions, 2003.
  9. The Primer (actually it’s a tutor which is a super-computer built with nanotechnology) in The Diamond Age : Or, a Young Lady’s Illustrated Primer by Neal Stephenson, 2000.
  10. Aristotle, The Personal Tutor, Aristotle (The Knowledge Web) on John Brockman’s Edge, Danny Hillis, 2000. This isn’t nominally a work of fiction, per se, but is sufficiently speculative and criticized for not being realistic, that it can be considered as at least a close cousin of fiction.
  11. Unnamed bio-nano-agent creatures in Prey by Michael Crichton, 2002. Check out the extensive bibliography that shows how Crichton at least started with some specific research efforts.
  12. R2-D2 and C-3PO, the robot “droids” in Star Wars. Not their “robotic” aspects, but the degree to which they are capable of autonomous decision and action.

All of these synthetic characters have captivated readers and viewers, but there are some problems:

  1. The capabilities are still way beyond current technology and depend on more dramatic advances in computational intelligence.
  2. The capabilities are more like dumb people than smart machines.
  3. The characters are rather two dimensional and not very rich.
  4. The focus is on human-like characters and simple tasks rather than on automating goals and reasoning. — ???

What’s Next

Much more research is needed.

Much more lab-bench trial and error experimentation is needed.

People need to identify key tasks or goals that they desperately want and need to have automated.

Users need to be provided with preliminary software which allows them to begin to get comfortable with building up a personal knowledgebase that can be used by software agents.

The Plan

Dream on! Both literally and figuratively. Given the vast amount of research and infrastructure development needed for this ambitious vision of exploiting the power of software agent technology for consumer applications, it’s way to early to be thinking about a concrete “plan” for implementing the full vision.

By all means, the research agenda should be pushed as hard as possible. There’s lots of dreaming to do there.

Occasionally, some dreamers will in fact attempt to implement pieces of their dreams, and on occasion they will even succeed. Over time, we will slowly creep up the side of the mountain, but rarely will any single innovation or even collection of innovations take us more than a small distance towards the summit. Only over extended periods of time will we see macro-level progress, which is the sum-total of the many efforts of many individuals and many teams.

There is no fixed plan and there cannot be. We need to be opportunistic and exploit possibilities as rapidly as we become aware of them, while simultaneously always dreaming of the next big quantum leap.

So, dream on!

TBD: A real roadmap with milestones.

Where to Start

Seriously, there is a lot of work to do and it cannot be done all at once in parallel. Much additional attention needs to be given to deciding which corners or niches of consumer applications of software agent technology will do the best job of getting the ball rolling.

A lot of infrastructure is needed. On the other hand, research on many of the higher-level capabilities can be performed with far less than a complete implementation of the lower-level infrastructure.

The distributed knowledge infrastructure deserves a lot of early attention. How does a user create new knowledge and put it out on the Consumer-Centric Knowledge Web? Shifting away from vendor-controlled servers is an interesting problem.

A robust implementation of the Distributed Virtual Personal Computer (DVPC) would get a lot of balls rolling.

More… TBD

References

There have been any number of conference papers, project descriptions, trade media articles, and even general media articles pontificating on the wonderful future of intelligent agents that’s always “just around the corner”, but somehow those corners are far more difficult to negotiate than we can ever seem to grasp. Each of these articles should have a variation of the standard passenger-side car mirror warning: “Objects are further than they appear.”

I’ve tried to find books related to the use of software agent technology for consumer applications, but they’re limited to the primitive existing applications I’ve listed at the beginning. There are plenty of books relating to industrial applications (see my list). Sad to say, the only books espousing an advanced vision as envisioned here are the works of fiction that I’ve listed.

aire (Agent-based Intelligent Reactive Environments) — An MIT CSAIL project dedicated to examining how to design pervasive computing systems and applications for people. To study this, aire designs and constructs Intelligent Environments (IEs), which are spaces augmented with basic perceptual sensing, speech recognition, and distributed agent logic. aire’s IEs have encompassed a large range of form factors and sizes, from a pocket-sized computer up to networks of conference rooms. Each of these serve as individual platforms, or airespaces on which pervasive computing applications can be layered. Examples of aire applications currently under development include a meeting manager and capture application, contextual and natural language information retrieval, and a sketch interpretation system (developed by the Design Rationale Group).

Project Oxygen — MIT’s pervasive computing project.

FRODO (“A Framework for Distributed Organizational Memories”) — a project focused on methods and tools for building and maintaining Distributed Organizational Memories (DOMs) in a real-world enterprise environment. It is a successor project of the DFKI KnowMore and VirtualOffice projects. The technical approach is based upon an application-driven combination of techniques from: agents for workflow enactment and information access, ontology acquisition from texts and user interaction, and document analysis and understanding.

…[TBD]

Raw Notes and TBD

Some of the following items need to be integrated into the main body of this paper, but others are merely needed as a check list that the appropriate topics are covered in sufficient detail.

  1. collective semantics
  2. a collective
  3. community building
  4. computer science research and development needed for foundation
  5. intelligent room, building, environment
  6. smart spaces
  7. contextual clues
  8. vendor-neutral apps
  9. minimize GI/GO
  10. information exchange
  11. facilitate targeted and desirable advertising
  12. consumer-oriented search engines
  13. multi-player games, MUDs
  14. search for meaning
  15. cooperation, competition, discovery
  16. community workspaces
  17. keeping consumer profile private (not centralized)
  18. monitoring personal security
  19. no more uploading
  20. viruses vs. marketing, bad code, misguided efforts
  21. progress and evolution
  22. organizing, sifting, haystack
  23. how to extend for organizational and enterprise use, vendors, alliances
  24. where to draw the line for this spec, clean it up, and distribute version 1.0
  25. analogous apps for Flickr, Friendster, LinkedIn — decentralized, distributed, P2P, etc.
  26. auto-Mensa group problem solving
  27. clubs, associations — prestige, exclusivity, revenue opportunities
  28. beyond structured query — cues
  29. distributed wikipedia, open directory — bypass web politics
  30. evolution of bits, bytes, files, folders, drives, systems, networks, users, communities — agent-oriented
  31. global data places, global database
  32. what is the opposite or complement of “base” as in “database”
  33. global demand pull — publish data to community (e.g., RSS) and “let it flow” — feed, caches, subscriptions
  34. prompt for knowledge
  35. seamless hybrid of human and agent effort
  36. clouds and emergent clouds
  37. being intentional
  38. identity tokens — subset for purpose
  39. difference detection
  40. transitive closure, composition, intellectual leverage
  41. managing secrets
  42. managing “The Big Picture”, top to bottom, inside out
  43. clear metaphor for agents as adjunct to the human mind
  44. issues related to the distinctions between bits and atoms
  45. the flip side of long-range vision: the immediate “feel”
  46. beyond IM/email: always communicating knowledge
  47. facilitation of coaching and mentoring
  48. personal information agents
  49. fiction: Impermanence Agent
  50. C2C ecommerce
  51. vetting: trust, reputation
  52. game theoretics
  53. Eliza: mock intelligence
  54. URI/anchoring: considered harmful
  55. reliably identifying communities
  56. name tokens, token servers
  57. server-free network computing
  58. reference vs. identity
  59. real world vs. digital identity
  60. evolving roles of money, value, pricing, cost, wealth, options, opportunity
  61. consumer federation
  62. consumer process flows
  63. game and decision theory — open games, dynamic equilibrium
  64. sensor networking
  65. managing the home
  66. context information
  67. effectively hiding complexity — agents to manage it
  68. free, cost, subsidy, deficit, reckoning
  69. opinion management, polls, surveys — value, compensation
  70. fanatics, zealots, and anarchists
  71. market-based mechanisms
  72. strong AI — not!
  73. self-organizing organizations
  74. autonomic networking
  75. emergence, emergent communities
  76. project workflow
  77. consumer collaboration on application development vs. “the developer” and “the development team”
  78. [natural] application aggregation
  79. consumer as a sovereign
  80. transparency: what’s happening and why
  81. tolerance for mistakes — lower the cost, make it easier to recover
  82. managing preferences
  83. focus on knowledge rather than specific apps — “knowledge-based applications”
  84. actor identification — who’s who and who’s in charge of what
  85. placement agents
  86. deep keywords — e.g., “job opportunities”, narrowing implicitly
  87. “Some people.”
  88. my agents are always on it even if I don’t know about it yet
  89. evolution of servers, virtual servers
  90. stuff finding us rather than us finding stuff
  91. diffuse network traffic — web servers considered harmful
  92. thinking (and typing) in knowledge
  93. the ultimate app: free-form entry of knowledge snippets
  94. triggers and workflow alerts
  95. nexus facilitation, based on information, not location
  96. facilitate working smarter rather than working harder
  97. information-based distributed computing rather than location-based computing
  98. economics: spectrum of subsidy from full payment for services to controlled, selective disclosure of consumer interests to vendors
  99. economics: software agents that read advertising streams and pass items of interest back to consumer
  100. facilitating the search for truth
  101. what is truth, what is true, what might be true, what might be false, what is likely to be true, what is unlikely to be true?
  102. autonomous agents vs. multi-agent systems
  103. disruptive technologies vs. disruptive consumers
  104. virtual bits
  105. blending software and services
  106. consumer media
  107. levels of knowledge management
  108. cooperative information agents
  109. Neopet as a next generation of the “Bob” concept
  110. meta-learning
  111. where do agents run?
  112. where do agents store their knowledge
  113. email — where knowledge goes to die
  114. common sense knowledge
  115. domain-specific ontologies vs. upper-level ontologies
  116. coping with hackers
  117. agent systems, cooperating and collaborating agents
  118. elaborate on “mobile agent”
  119. nature of agent “thought processes”
  120. focus on complexity of “systems” — even a single consumer is a complex system
  121. systems: mutual interaction of parts
  122. the whole represents more than the sum of its parts and has a synergy of its own
  123. role of context
  124. emergence in systems
  125. understanding vs. knowledge
  126. truth vs. “a” reality and multiple realities
  127. accretion of knowledge vs. explicit collection
  128. collaborative applications
  129. agent-filtered advertising
  130. consumer benefits: productivity, enjoyment, more knowledgeable
  131. consumer electronic manufacturer interest
  132. how to be “in-touch” with the consumer market
  133. consumer-centric software agent technology
  134. distributed knowledge and infrastructure
  135. what do consumers consume and what do they produce?
  136. virtual organizations
  137. journeys of discovery
  138. web intelligence (WI)
  139. wisdom web and wisdom web agents
  140. knowledge ecosystem
  141. role of digital life
  142. active media technology (AMT)
  143. agent views of vendors as outside entities
  144. consumer protection via agents vs. government and lawyers
  145. the consumer “domain”
  146. what is the precise definition of “consumer” that excludes businesses and other entities that “consume” goods and services as well, not to mention authorities who are in fact consumers but have a heavy “authority” presence (e.g., political leaders, business executives, etc.)
  147. Consumer Grid
  148. consumer choice — rational or not?
  149. equilibrium vs. disruption
  150. distinctions and similarities between challenges and opportunities
  151. organizing around individuals — democracy centered around consumers
  152. define: Who is a consumer?
  153. Roles and methods of linking
  154. knowledge vs. memories
  155. where do a consumer’s memories live?
  156. My Intelligent Agent(s) / Your Intelligent Agent(s)
  157. coping with hype
  158. images, illusions, surfaces, depth, distortion
  159. bridging the voids between types of media (text, image, audio, visual, data, time)
  160. coping with large volumes of media, even for narrow domains and niches
  161. collective action
  162. digital relationships
  163. creative commons
  164. the interface, intersections, conflicts, and collisions between culture and technology
  165. coping with the signal to noise ratio
  166. the nature of community
  167. what are you thinking?
  168. what are you feeling?
  169. what do you want?
  170. metadata, social and otherwise
  171. facilitate converation
  172. the nature of conversations
  173. the nature of participation
  174. coping with the relations between our past(s), present(s), and future(s)
  175. power of randomness — now for something completely different
  176. concept map/cloud/network/web
  177. facilitate storytelling
  178. Adopting knowledge, putting knowledge up for adoption
  179. knowledge seed for the knowledge tree of life
  180. consumer use of software agents for knowledge-based computing < 0.0001% of Kurzweil’s Singularity
  181. establishing and maintaining knowledge connections vs. device connections
  182. facilitate access to libraries, digital libraries, knowledge libraries
  183. facilitate grasping of knowledge, the complex, the simple and the seemingly simple
  184. questions as knowledge
  185. role of proof, certainty, and evidence
  186. coping with gossip
  187. try to find some books for reference
  188. managing hope and expectations
  189. coping with process vs. structure
  190. facilitating use of analogies
  191. just works, seamless
  192. identifying opportunities from both hot topics and hidden values
  193. content management vs. knowledge management
  194. some hidden category between knowledge and wisdom
  195. facilitate intuitive leaps and emotional intuition
  196. facilitate pattern recognition
  197. much more work on the economics, especially for non-visible agents
  198. the nature of knowledge
  199. extend your subconscious
  200. invisible servant
  201. remembrance agent
  202. app model: behind the scenes, automated
  203. talk to my knowledgebase
  204. scenarios and stories
  205. shared context
  206. focus on platform and knowledge infrastructure, not specific apps
  207. consumers own their data
  208. facilitate serendipity
  209. knowledge “publish and subscribe”
  210. facilitate enlightenment
  211. “aspiring” vs. “aspirations”?
  212. coping with priorities and sense of urgency
  213. consumer computing
  214. consumer-oriented knowledge-based software agent environment
  215. knowledge-centric versus site/location-centric
  216. role of music
  217. information retrieval (IR)
  218. facilitate “seizing the day”
  219. coping with procrastination
  220. coping with resolutions (e.g., New Years)
  221. facilitate a brighter today rather than the diffusion of a brighter tomorrow
  222. facilitate turning time into the timeless
  223. facilitate a journey into meaning
  224. gaining perspective
  225. facilitate discovery of possibilities
  226. paradigm of “tapping into” the knowledgesphere
  227. knowledge as a platform
  228. JIT knowledge
  229. auto-research
  230. knowledge as active data, not static
  231. flexibility vs. Mt. Rushmore syndrome
  232. coping with crises
  233. verbs vs. nouns — active processes vs. static results
  234. scenarios: options, risks, evolution of goals
  235. URI vs. UKI (context-independent, context-relative)
  236. the problems with web-site-centric URIs, alternatives
  237. parallel operation and asynchronous operation
  238. semantic markup
  239. velocity and acceleration
  240. organize by technology, specific app areas, variety of app “thrusts”, common app characteristics
  241. friends, neighbors, family, relatives, community interactions
  242. need for both formal names (specifications) and user-friendly nick names
  243. elaborate on identity issues
  244. agreements, computational agreements
  245. connecting knowledge, bridging gaps
  246. essences, core knowledge, meta-knowledge
  247. gatherings, meetings, online conferences
  248. grounding knowledge
  249. consumer reviews
  250. smart information (vs. knowledge)
  251. diagram: platform, apps, knowledge flow, storage, consumer, vendors
  252. knowledge mashup
  253. facilitate feedback
  254. facilitate monitoring credibility
  255. facilitate connecting dots
  256. Auto-list
  257. what are current consumer “pain points”? — identity theft, privacy, ease of use, comfort, confusion, …
  258. the “real” Semantic Web
  259. facilitate strategic planning
  260. principles and and concepts for consumers
  261. facilitate unique sense of consumer identity
  262. facilitate picking and choosing and managing choices
  263. pathways, evolution, “knowledge paths”
  264. what do you/we really want?
  265. facilitate surrogate travel
  266. concept/term syntax vs. URI
  267. blog for this vision
  268. Semantic Web as an underlying storage/transport mechanism, but not the “true” knowledge layer
  269. knowledge as transparent ubiquity
  270. who is the intended audience for this vision? and potential customers
  271. quantum computing — the knowledge angle
  272. quantum information — quantum knowledge?
  273. entangled knowledge
  274. coherent knowledge
  275. knowledge complexity — modeling, measuring, reporting, managing, reducing, coping
  276. robust knowledge, fault-tolerant knowledge
  277. consumer white list
  278. e-commerce access to consumers
  279. develop use cases
  280. 2-page position paper (for conferences, workshops)
  281. facilitate persuasion
  282. facilitate creative writing, role-playing, plays
  283. infosphere
  284. agent system vs. MAS
  285. place-shifting
  286. consumer rules (ala business rules)
  287. consumer products with embedded agent-based systems
  288. impact on society
  289. letting knowledge webs drive infrastructure software design — knowledge-based software design
  290. gadgets and devices, geeks and consumers
  291. Semantic Web and devices
  292. consumer-centric consumer devices
  293. vendor-independent/neutral computing
  294. consumer-controlled computing
  295. introduction for consumer-centric knowledge web
  296. coping with and exploiting implicit relationships
  297. consumer control
  298. consumer products with embedded agent-based systems
  299. consumer-centric knowledge vs. consumer-oriented knowledge vs. consumer knowledge
  300. consumer-centric knowledge-based applications
  301. what is the real “juice” that will drive consumer-centric knowledge
  302. consumer design (ala industrial design, but driven by and focused on the consumer) — consumer-centric design
  303. coping with emergencies
  304. intellectual capital — value
  305. knowledge as predictions
  306. non-text media knowledge
  307. auto-web site
  308. beyond answers, questions and knowledge
  309. facilitating engagement
  310. facilitating limited vendor access
  311. what is knowledge versus information?
  312. ontology vs. schema (ERD)
  313. relationship between knowledge and environment (and entities)
  314. RFID?
  315. digital lifestyle
  316. facilitate the connection, contrast, and separation of real-world and computational knowledge
  317. what is computational knowledge?
  318. you own your knowledge, your awareness
  319. facilitating structure and focus *and* facilitating free-flow
  320. facilitating journaling
  321. facilitate unraveling of mysteries and confusion
  322. “knowledge buddies”
  323. “knowledge tribes”
  324. facilitate finding your tribe(s)
  325. knowledge as adventure
  326. knowledge as narrative (story)
  327. “laws” of consumer-centric software agents
  328. consistently use consumer-centric software agent term
  329. knowledge capture: how, better models
  330. meaningful knowledge
  331. facilitating good ideas
  332. coping with “good” ideas
  333. an index or topic map
  334. facilitating paradigm shifts
  335. coping with paradigm shifts
  336. facilitating communities of practice (CoPs)
  337. facilitating communities of interest (CoIs), implicit as well as explicit
  338. interaction with and awareness of nature, the real world outside of human activity
  339. the agent paradigm
  340. portals, libraries, and directories
  341. common knowledge
  342. common concerns, common values — “auto” groups
  343. addressing pressing social problems
  344. facilitating reasoning
  345. generic application capabilities — factoring from specific applications and categories
  346. opportunities for distinctions between information and “content”
  347. mention and link to Agtivity.com, the concept web
  348. PDF version of this paper
  349. rationality and self-interest
  350. facilitate being concise
  351. agreements between individuals, within groups, and among groups
  352. maintaining attention
  353. joining ideas together
  354. gaining clarification
  355. response, thread of conversation
  356. highlighting
  357. knowledge: what’s the point?
  358. knowledge/idea/concept ecology
  359. intentional knowledge vs. incidental knowledge or accidental knowledge
  360. knowledge: linking and connecting
  361. who is “we”, “us”, “them”?
  362. ad-hoc, agile knowledge
  363. integration
  364. culture development
  365. messages
  366. transparency, opaqueness
  367. informing
  368. managing goals
  369. getting things done
  370. reporting
  371. the magic of threes
  372. study, contemplation, thought
  373. blogstream
  374. knowledge silos
  375. requesting and giving direction
  376. leveraging — as a generic capability or measure
  377. comprehending consequences
  378. visionary
  379. managing priorities
  380. participation — facilitating, coping
  381. filters, filtering
  382. sources, reliability
  383. abacus analogy for knowledge
  384. webs of inclusion
  385. institutional memory, knowledge
  386. knowledge planning — as if it were active and could be tracked
  387. outcome-oriented knowledge
  388. hybrid knowledge
  389. framing
  390. knowledge-driven processes
  391. knowledge wiring diagram
  392. strategic knowledge, knowledge of strategy
  393. feedback, guidance
  394. norm, norming — facilitate, coping
  395. what makes sense?
  396. service: essential, desirable, discretionary
  397. understanding costs of choices
  398. facilitate noodling
  399. budgeting as a specific application
  400. blissful ignorance
  401. parking lot(s) for issues
  402. facilitate community sustainability
  403. streamlining processes
  404. facilitate brainstorming
  405. facilitate vetting ideas
  406. constraints: identifying, managing, coping, utilizing
  407. email: content/knowledge, conversations/interactions, tracking/status
  408. hard truths
  409. truthiness
  410. sidebars in threads of discussion
  411. coping with folklore
  412. thinking in XML/knowledge fragments
  413. building knowledge chains, trees, webs
  414. role(s) of standards
  415. statistical analysis, pattern recognition for knowledge mining
  416. Beyond the Cluetrain Manifesto — consumer-centric, not merely consumer-oriented
  417. knowledge: structure, generation, dissemination, transferal, digestion, use, modification, reuse
  418. role of philosophy, personal philosophy, representing and using philosophies
  419. philosophical basis for knowledge: in general, and in particular
  420. identification and reference: identity, meaning, and reference
  421. linguistic theories
  422. social meaning
  423. rules: all aspects
  424. auto-rules — manually-constructed rules are too difficult to get right, to comprehend, to maintain, and to evolve
  425. knowledge versions
  426. knowledge constraint management
  427. knowledge appliance
  428. topic versus concept
  429. degree of conceptual precision
  430. loose knowledge versus tight knowledge: what is or isn’t known about tightness, ambiguity, context
  431. two-way knowledge expression: let a thousand parsers and text generators bloom
  432. link to Agent Technology Roadmap
  433. x versus management of x versus decisions about x
  434. theory of it all
  435. organizational memory (OM), distributed organizational memory (DOM)
  436. how close will the consumer knowledge web be to actually thinking?
  437. facilitating mutual respect
  438. Cyc “common sense” knowledge system
  439. pervasive knowledge
  440. adaptive knowledge
  441. ambient knowledge
  442. context-aware knowledge
  443. coping with subtlety
  444. facilitating philosophical inquiry
  445. annotation of knowledge
  446. facilitate investigation
  447. granularity of knowledge
  448. facilitate open knowledge
  449. WisdomArk.com
  450. interoperability of knowledge
  451. facilitate negotiation
  452. decentralized knowledge
  453. managing event calendars, personal calendars, and group calendars
  454. tenets of the CCKW
  455. social network analysis
  456. cooperative structures
  457. the knowledge economy
  458. knowledge workers
  459. coping with the elite, the high-priests
  460. facilitating systematic organization of knowledge and activities
  461. ethics and ethical knowledge
  462. how to make CCKW “viral”, promote adoption
  463. culture and cultural cognition
  464. express the consumer experience
  465. need a better acronym
  466. agencies: agents grouped by competencies — grouping consumer interests
  467. organizational memory
  468. surprise me — power of random behavior
  469. detail application categories
  470. detail app platform levels
  471. auto-forms — knowledge drives information gathering requirements
  472. beyond the bleeding edge, for now
  473. variants of the Consumer Web: presentation, information, knowledge, intelligent
  474. joint goal-seeking
  475. compiling and linking knowledge
  476. how to make the CKW appealing
  477. relationship between knowledge and intelligence
  478. knowledge, intelligence: what is the third leg of the stool? agents? consumers? the environment?
  479. linkage between knowledge and consumer activity
  480. list what consumers do or do not want to do or should do — norms
  481. facilitate escape, membership
  482. embracing knowledge
  483. skepticism vs. knowledge
  484. transparency of technology for the consumer knowledge web
  485. horizons: the view, pushing towards them, pushing past them, expanding them
  486. what will it look like? It won’t. The larger you build your knowledgebase, the more agents will be able to do without your intervention.
  487. managing demands for attention
  488. coordinating activities
  489. knowledge analogy to the classic PIM (Personal Information Manager): the Personal Knowledge Manager?
  490. knowledge analogy to Ajax, a group analogy as well
  491. no more “synchronize”
  492. deep context: more than simply superficial info
  493. Always On → Always Knowing
  494. knowledge portals
  495. obliteration: removal of mistaken or offensive knowledge — really?
  496. scope and scoping — as knowledge-generating activities
  497. memorialization
  498. recommendations
  499. importance: priority vs. significance
  500. hypotheticals
  501. impacts of potential actions
  502. initiative
  503. need a consumer-oriented vocabulary, jargon, lingo, slang, dialects
  504. abstraction vs. specificity
  505. abstraction vs. ambiguity
  506. how to model abstractions — let software agents do it
  507. interactions between consumers and governments and other organizations
  508. process — as forms of knowledge
  509. agendas
  510. forward updating of knowledge
  511. is knowledge ever really “consumed”?
  512. cost(s) associated with knowledge
  513. forbidden knowledge, classified knowledge
  514. probability, statistics, and uncertainty — statistical nature of knowledge
  515. the knowledge “agora” must feel comfortable and vibrant, not like a library, archive, or vault
  516. “knowledge vault”
  517. coping with fragmented intelligence
  518. “external” knowledge
  519. knowledge contained in images (or alleged) — “A picture is worth a thousand words” — challenge: how to access those thousand words
  520. pictures as symbols
  521. pictures vs. the language barrier
  522. the knowledge barrier
  523. panoramas, scenarios
  524. synonyms
  525. dirty knowledge, knowledge cleansing
  526. truth, lies, deceit, deception, fiction, wishes, hopes
  527. agents of ignorance vs. agents of truth
  528. completeness, accuracy, consistency, utility
  529. knowledge having a life of its own vs. “owned” knowledge
  530. theology
  531. knowledge communities
  532. building blocks for knowledge
  533. units of knowledge
  534. content vs. knowledge
  535. challenges, passions as forms of knowledge
  536. deployment of the CKW with today’s technology will result in ineffectual non-solutions and cause more problems than it solves
  537. InnoCentive — web-based community for scientific collaboration (“scientific challenges) — a model for some consumers
  538. objects vs. ideas vs. forces and tendencies
  539. trivia and factoids vs. substantial knowledge
  540. coping with kooks
  541. correlation vs. causality
  542. agents in knowledge (e.g., causes, causal agents)
  543. coping with difficult problems
  544. blank space vs. knowledge — knowing what we don’t know
  545. cooperative tasks
  546. classification
  547. knowledge processor, personal knowledge processor, group knowledge processor, domain knowledge processor
  548. the blank slate, tabula rasa — the power of, coping with
  549. Knowledge Machine, Mindstorms (Seymour Papert)
  550. how do we learn, how do we teach?
  551. economics of learning
  552. babbling
  553. memory loss
  554. computer-assisted instruction (CAI)
  555. strength of memory
  556. idea processor
  557. powerful ideas
  558. intersection of consumer and academic knowledge
  559. how will consumers actually use the consumer-centric knowledge web?
  560. COW: Consumer Ontology Web
  561. Ontolite: a simplified methodology and language for expressing ontologies
  562. coping with propaganda and social control
  563. alignment of disparate knowledge communities: values, ontologies, views
  564. target vs. source, here vs. there, us vs. them
  565. explicit vs. implicit knowledge
  566. the nature of knowledge and intelligence
  567. what is an “answer”? how to manage “answers”
  568. registering interest
  569. better case for why agents vs. traditional or Web 2.0 approaches
  570. more on contrast with “The Singularity”
  571. parity between producers and consumers
  572. net on the “edge”, consumers in “middle”
  573. benefits to society
  574. channels, broadcasts
  575. conclusions (vs. facts)
  576. the initial data points: Web, Semantic Web, Cyc, Singularity, classic AI
  577. “total knowledge”
  578. downside of wider access to knowledge
  579. ontology alignment
  580. the classic MVC model, with knowledge as the fundamental model
  581. methods, methodologies, approaches, etc. as knowledge
  582. coping with dogma
  583. support for knowledge or an argument
  584. arguments
  585. nature and role of reification in knowledge processing
  586. subsetting for kids, children
  587. representing novelty
  588. distilling knowledge: not just “nuggets”, but wisdom and opportunities
  589. coping with anarchy
  590. target the “next” generation: those who will be 8-years old in ten years — skip the current generation and their parents
  591. disruptive technology: disrupt current disruptions
  592. “consumer deep”: current apps way too superficial
  593. knowledgecast: what would it be like?
  594. knowledge as liberation
  595. distinguish economically-motivated knowledge
  596. distinguish goals from constraints (positive vs. negative)
  597. emulation
  598. facilitate general planning
  599. structured consumer information as distinct from unstructured knowledge
  600. Cyc: too primitive, low level, and fragile for consumer use
  601. “That’s not what I meant”
  602. do what I mean (DWIM)
  603. visualizing concepts
  604. diffusion, tangentials
  605. knowledge variables, functions
  606. mnemonics, memory aids
  607. meta-meta-knowledge
  608. dealing with criteria for best/better
  609. tacit knowledge, how to access it
  610. how do consumers think about knowledge
  611. “that’s just semantics”, bad semantics, insensitivity to semantics
  612. facilitating focus
  613. sources of knowledge
  614. ripple effects
  615. “connecting” with knowledge
  616. analog to a synapse
  617. mind vs. brain
  618. knowledge roadmaps, designs, schemes, outlines
  619. knowledge catalog
  620. objective vs. subjective knowledge
  621. likelihood
  622. FAQs as knowledge
  623. speaking each other’s language
  624. the MIT Logo experience, kids, early learning, teaching
  625. beyond the Cluetrain Manifesto — consumer-centric, vendors do the compromising to gain access to consumers
  626. KT: Knowledge Technology (ala IT)
  627. major challenges: ontology alignment
  628. C-KOW: Consumer Knowledge Ontology Web
  629. death of email: email -> “create knowledge”
  630. knowledge definitions: collect them
  631. can you possess and make sense out of knowledge without ontology?
  632. ontological commitment
  633. unit/atom of knowledge — what is the least you can know?
  634. does knowledge want to be free? what does that mean, entail?
  635. remix/mashup knowledge
  636. assertions vs. facts
  637. power mining, automatic ontology mining
  638. knowledge claims
  639. creative destruction applied to knowledge
  640. economic signals
  641. activity-oriented knowledge (e.g., hobbies, games, sports, clubs)
  642. context for focusing agents
  643. knowledge as a form of currency
  644. nature of a piece of knowledge
  645. correlating fragments of knowledge
  646. knowledge as threads or strands, strand length — strands of knowledge
  647. analogy of nerves and neurons: axons and dendrites
  648. fingers/arms of knowledge
  649. facets, purposes
  650. using knowledge without “knowing” it
  651. relevance is relative — Theory of Relativity for knowledge
  652. navigation through knowledge/ignorance
  653. libraries of ignorance
  654. capturing judgment
  655. role of traditional libraries — acquiring and organizing books, knowing what’s where
  656. grapevine, gossip, water-cooler “knowledge”
  657. knowledge grazing
  658. subjects matter experts
  659. eLearning — knowledge about your level of knowledge
  660. virtual knowledge
  661. knowledge templates
  662. intuition — what does it really mean — meaning without knowing
  663. deep study
  664. discussion re: knowledge acquisition, dissemination
  665. variation from traditional “field of computation”
  666. what is the nature of “constructing” knowledge?
  667. semantic wiki, knowledge wiki
  668. personal knowledge management system
  669. non-technical users — what does that really mean?
  670. role of structured text
  671. “brewing” knowledge — tea metaphor
  672. social network intelligence
  673. agent intelligence
  674. software agents that can “work” with knowledge without comprehending its full meaning — knowledge as the more appropriate “unit” for software agent activity
  675. knowledge topology
  676. ignorance as the unit/seed of knowledge — ignorance + desire + motivation + aptitude + skill + persistence
  677. role of motivation in knowledge, role of knowledge in motivation
  678. web farming
  679. the nature of the role of a “web” in knowledge and software agent activity
  680. knowledge foraging, rummaging
  681. distributed problem solving
  682. macro knowledge vs. micro knowledge
  683. role of clustering, both that which is inherent in the structure of knowledge, and when querying
  684. the significance of The XML Revolution
  685. channel concept — pro vs. con
  686. model description
  687. knowledge is ubiquitous
  688. dictionary as starting point for knowledge, plus specialized glossaries
  689. grasping, comprehending
  690. being stuck or hung up on “bad” knowledge
  691. liana as threads or vines, “knowledge vines”
  692. jungle metaphor, The Knowledge Jungle
  693. information flow, knowledge flow
  694. concept vs. keyword
  695. unit of concept
  696. concept formation process
  697. semi-knowledge, semi-ignorance
  698. learning processes
  699. knowledge-deficient environment
  700. knowledge environment
  701. what does it mean to introduce a knowledge infrastructure to consumers
  702. nature of introduction of concepts
  703. knowledge as overlapping “clouds”
  704. knowledge as planet, a solar system, a galaxy
  705. brain and nerves as a knowledge metaphor, nature of “computing” knowledge
  706. knowledge beast — it’s alive
  707. jokes, poetry, literature, prose as knowledge
  708. conventional wisdom
  709. digital revolution, the knowledge revolution
  710. knowledge colonialism
  711. knowledge sector
  712. text -> information -> knowledge
  713. mobile knowledge, position/context/locale/environment-dependent knowledge
  714. knowledge is power, power is knowledge
  715. questioning knowledge
  716. challenging basic assumptions
  717. call-center management, knowledgebase
  718. relating concepts
  719. dangerous ideas
  720. nature of analyzing knowledge, knowledge analytics
  721. knowledge warehouse
  722. PowerPoint as a knowledge metaphor, bullet points as a knowledge metaphor
  723. knowledge modules
  724. nature of learning, nature of teaching
  725. knowledge vs. content
  726. central thesis: this is the best way to go: 1) best benefit for consumers, 2) best use of software agent technology, 3) best angle on support for knowledge
  727. briefings as knowledge
  728. knowledge vs. experience
  729. QA/QC for knowledge
  730. is it fiction to keep consumer and vendor knowledge separate?
  731. nuance
  732. risk
  733. reputation: establishing, validating
  734. video knowledge
  735. waves, vectors
  736. impact of the laws of physics on knowledge (meta-knowledge)
  737. harvesting knowledge
  738. “wisdom of crowds”
  739. knowledge-sharing tools
  740. knowledge outreach vs. search
  741. knowledge web as a marketplace with non-monetary, but functional economics
  742. what is the invisible hand of knowledge
  743. ideology and knowledge: impact, constraint, influence
  744. illusions: imperfect knowledge
  745. the dark side: dysfunctional knowledge or knowledge used to enable dysfunctional behavior
  746. how to avoid or copy with knowledge “running out of control”
  747. knowledge about human nature — is it special?
  748. empowerment
  749. feeling that we can make a difference — existing knowledge not a monolith
  750. coping with and resisting commercial pressures and influences
  751. coping with and resisting efforts to “control” knowledge
  752. safeguarding knowledge
  753. fear of knowledge: understanding, coping, adapting
  754. knowledge of behavior: what do we really know?
  755. relation of knowledge to human consciousness
  756. upheavals of thought: coping and even facilitating
  757. encoding the history of ideas
  758. the cosmos: nature, structure, and evolution
  759. drives and motives
  760. the nature of adaptation of knowledge
  761. group-limited knowledge (scope)
  762. prevailing knowledge: coping with and acknowledging
  763. “rooms” and “houses” of knowledge: boundaries and barriers
  764. knowledge landscape, terrain
  765. heterogeneous nature of knowledge
  766. list of people and place names as part of core
  767. world almanac as part of core
  768. thesaurus as part of core
  769. core lists need to be structured, eventually, but provide an initial base of initial concept “points” (ala URIs)
  770. “knowledge” contained in popular non-fiction books
  771. “knowledge” contained in works of fiction
  772. cultural artifacts as knowledge
  773. archiving of knowledge — knowledge snapshots
  774. nature of noise in knowledge
  775. what does it mean for knowledge to be “digital”?
  776. analog as a superior model for knowledge compared to digital
  777. approximating analog knowledge using digital techniques — pursue ever-finer granularity
  778. personalized knowledge: MyKnowledge, OurKnowledge
  779. role of psychology in knowledge
  780. extrapolation, interpolation
  781. inference: what the knowledge web does vs. what the consumer does
  782. dangerous inferences
  783. knowledge vs. policies, social policy
  784. memes, anti-memes
  785. unspeakable ideas
  786. experiments: role in generating, testing, and building knowledge
  787. rational autonomy
  788. modes and differences in rates of knowledge uptake
  789. personal science, worldview
  790. walled gardens
  791. intermediation: coping and facilitating
  792. explanations vs. knowledge
  793. power of examples, limitations of examples
  794. knowledge engines
  795. approaches to accelerating knowledge uptake
  796. rumors
  797. focusing on categories
  798. news as knowledge
  799. chatter, ramblings, rants as knowledge
  800. levels of description
  801. personal theories, consumer theories
  802. deliberation
  803. guiding forces
  804. knowledge in complex adaptive systems (CAS)
  805. dark knowledge: it’s there, but we don’t sense it
  806. mechanisms of association of concepts, symbols, and meaning
  807. meaning vs. value
  808. conformity vs. dissent
  809. blueprints, “genes”
  810. knowledge of computation
  811. knowledge in the context of virtual reality — virtual knowledge?
  812. coping with cunning — attempts at excessive control
  813. facilitating free-will
  814. a day/week/month/year/life in the life of a consumer
  815. as the Web grows, signals get weaker and noise gets stronger
  816. the unknown — acknowledging and coping with it
  817. human needs
  818. wonder — acknowledging and facilitating
  819. the edge, the frontier — the difficulties
  820. not knowing — an expectant knowledge
  821. accommodation of disparate knowledge
  822. limits of awareness
  823. managing lists, fascination with them
  824. what are the inherent and potential limits of a consumer-centric knowledge web (e.g., expressive power)
  825. knowledge-exchange file formats, embedding knowledge in a PDF
  826. knowledge as an object vs. knowledge as a process — integrating the two
  827. nuisances from the perspective of knowledge
  828. what is the “perspective of knowledge”?
  829. making sense of stories — what is the knowledge contained in a story?
  830. recognizing and representing synergies
  831. what is relevance?
  832. measuring utility of knowledge
  833. boundary between mental and non-mental
  834. knowledge of abilities vs. skills
  835. influence of knowledge on the human brain itself
  836. plasticity of the human brain
  837. wending: facilitating slow, tedious processes
  838. the human spirit
  839. facilitating connections between people and group as much as connections between disparate knowledge
  840. hard problems
  841. knowledge of human relationships, human nature
  842. what “what that might mean” means
  843. coping with change, the speed of change
  844. anticipation: acknowledging, managing, coping, facilitating, exploiting
  845. role of education in knowledge
  846. variable interpretations of knowledge
  847. spheres of knowledge, agents that move between them
  848. information impinging on our senses
  849. judging and measuring meaningfulness
  850. implicit knowledge, learning
  851. non-verbal knowledge
  852. career advancement
  853. declarative knowledge, non-declarative knowledge
  854. information environments, knowledge environments
  855. what does knowledge accomplish?
  856. knowledge choices: accept, reject, defer, tolerate, influence
  857. faith-based knowledge
  858. knowledge upheavals
  859. literacy and knowledge
  860. knowledge divides
  861. competency: acknowledging, coping, facilitating, managing
  862. great sources of knowledge
  863. gatekeepers: pros vs. cons
  864. demonstration vs. speculation
  865. degree of rigor, lack of rigor
  866. basic principles
  867. unknowables
  868. extra-terrestrial knowledge — yeah, right, but still…
  869. fitness functions for knowledge, faked-fitness
  870. “what if” — is it really a form of knowledge, or is it simply a dysfunctional form of knowledge
  871. nonlinearity of the human psyche
  872. the idea of ideas
  873. facilitating quests
  874. manifest knowledge — what is it really?
  875. fundamentals — if they exist at all
  876. central truths — if they exist at all
  877. fundamentally, what is a topic?
  878. knowledge contained in slogans, mottos, and one-liners
  879. what is the nature of novelty?
  880. shared understanding
  881. intergenerational alignment
  882. forgotten gems of knowledge
  883. conservation of mind-space — too much knowledge, too little space in our personal minds
  884. coping with cues
  885. understanding mechanisms — “why” — knowing why
  886. pre-linguistic concepts
  887. nature of emergence in all knowledge
  888. networks of knowledge: what is the nature of a network?
  889. role of hierarchy in knowledge
  890. role of surprise in knowledge acquisition and adoption
  891. role of authenticity — when is it no so important?
  892. role of insight, insight itself as knowledge, observation vs. insight
  893. the sport/game of knowledge
  894. justification for conjectures, beliefs, and theories
  895. when/can a knowledge web become aware of itself?
  896. implicit coordination of knowledge — coordinating knowledge without explicitly coordinating it
  897. predation and parental supervision issues for minors
  898. Danny Hillis’ Aristotle (The Knowledge Web) on John Brockman’s Edge
  899. levels of knowledge web: infrastructure, core knowledge, general knowledge, domain-specific knowledge, group-specific knowledge, global apps, general apps, domain/group-specific apps, personalized apps, etc.
  900. what is the knowledge equivalent of a flower, a plant, a tree, an insect, a bird, a bear, a mountain, a drop of water, a cloud, or even a planet?
  901. value and dis-value of “guided” learning/knowledge
  902. hunter-gatherer approaches to knowledge vs. farming vs. alternatives
  903. huge numbers: coping and facilitating with them
  904. “know what I know”
  905. the brain is a machine … what is a machine?
  906. to what extent is some knowledge instinctive or innate?
  907. does it make more sense to speak of being consumer-relative rather than consumer-centric?
  908. a homunculus as a model of human/human-like capability and perception — pros and cons
  909. CCKW synergism: empowers software agents and requires software agents as well
  910. knowledge is the “juice” needed to get software agent technology really moving
  911. facilitating the adoption (and adaptation) of new habits, coping with existing habits
  912. issues related to instilling new habits — pros and cons
  913. what is the nature of “instill” and how do we deal with it?
  914. issues related to motivating and avoidance of de-motivation
  915. Seymour Papert’s thoughts on (against) the concept of a super tutor or even an artificial tutor
  916. “knowing skills” (ala learning skills) — what does it take to “know”
  917. indirect attacks on The Knowledge Problem
  918. assessing “domain competence”
  919. parables as carriers of ideas [this is a very important idea]
  920. illiteracy: challenges and opportunities
  921. theses: 1) CCKW will eventually be doable, 2) much research is needed, 3) now is the time to do this research, and 4) implementation of CCKW will be feasible as this research progresses, over the coming decade
  922. will the knowledge web actually make us all smarter, or simply allow us to be dumber?
  923. upper ontologies and common ontologies and core ontologies to facilitate domain-specific ontologies, group ontologies, and personal ontologies
  924. situation ontology
  925. joy of learning, discovery vs. mere utility
  926. CCKW not smart enough to tutor consumers
  927. civic knowledge to facilitate citizenship
  928. what is analog knowledge?
  929. sustainability of a knowledge ecosystem
  930. filters as knowledge, tools as knowledge
  931. work “outside the box”
  932. coping with arrogance, arrogant knowledge
  933. coping with attitude
  934. consumer energy usage and attitudes as a potential app
  935. “the check is in the mail” — what does it (anything) mean vs. what does it (anything) *really* mean?
  936. pervasiveness of random processes — what does it mean to have knowledge about a random process?
  937. role of narrative and stories in knowledge
  938. limits on knowledge in general
  939. how might a general knowledge web be distinct from a consumer knowledge web
  940. “on its face” — what does this really mean, in a deep sense for knowledge?
  941. the inherent connection of knowledge to experience
  942. relationship or distinction between thought and knowledge
  943. explaining yourself
  944. proposals as a specialized form of knowledge
  945. animating meaning — turning knowledge into stories for evaluation and explanation
  946. the nature of abstract thought
  947. placebos: what’s the analogy for knowledge, if any? — knowledge as a “treatment”
  948. hormones: what’s the analogy for knowledge, if any? — making knowledge more appealing or effective
  949. relationships between levels or degrees of cognition and knowledge
  950. knowledge “mirrors” and mirroring and mirroring mechanisms
  951. is a knowledge web really a “disembodied mind” or is it something rather different?
  952. coping with difficult ideas — ascertaining true and significance, if even possible
  953. coping with fences, borders, abysses, and other obstacles to the pursuit of knowledge
  954. knowledge as a goal to pursue vs. knowledge as a continuing inflow of “ideas”
  955. subtle properties
  956. contrasting the “what” and “who” and “where” and “when” with the “how” and “why” and “what does it mean?”
  957. focus on problems and questions and curiosity and need
  958. authority for knowledge (as in authority of a source)
  959. the nature of migration of knowledge
  960. significance of age vs. youth in openness to knowledge
  961. role of skepticism — pros and cons
  962. what are the actual building blocks for a consumer-centric knowledge web, the foundation, the structure, and the infrastructure as well?
  963. is there really “too much knowledge”?
  964. specialization: coping and facilitating
  965. bridging between disciplines
  966. repackaging highly technical knowledge for popular consumption
  967. Vannevar Bush’s Memex
  968. what is “good enough” as a primitive, but useful approximation of the ultimate CCKW?
  969. how is a knowledgebase different from an information database, besides being distributed?
  970. coping with out-of-date knowledge
  971. knowledge of common problems
  972. human reinforcement of knowledge, machine-deduced or otherwise
  973. machine reinforcement of tentative human beliefs
  974. need and roles for human authors and knowledge engineers vs. automated knowledge engineers
  975. CCKW is a base for tutoring software, but not a tutor itself
  976. role of timing in knowledge
  977. gaining mastery of a subset of knowledge
  978. starting points for learning
  979. medium for publishing knowledge
  980. annotation: is it a distinct activity, or should it be integrated with the knowledge capture process itself?
  981. role of encyclopedias in the CCKW
  982. role of professional/refereed journals in the CCKW
  983. mapping and cataloging the CCKW
  984. human desire to share knowledge
  985. seals of approval, vetting (a form of authority) — is authority distinct?
  986. peer review — what does this mean in the context of consumers?
  987. potential for too much of the blind leading the blind?
  988. one person’s wheat is another person’s chaff
  989. power law effects in knowledge (small amount of highly-referenced knowledge, large amount of little-referenced knowledge) — not to be confused with authority
  990. is knowledge inherently free? is it ever really free?
  991. knowledge guides
  992. focus attention on early adoption by kids (ages 8–15) — more flexible and open to novelty
  993. how to broaden the spectrum of knowledge authoring capabilities
  994. initially build a knowledge web broadly, and then backfill niches, as opposed to focusing on narrow niches first
  995. role and nature of critical mass for a knowledge web — is it really an issue or not?
  996. underlying principles (for concepts and topics) — in relation to how we structure knowledge
  997. coping with hand-waving
  998. facilitating the opening of minds
  999. role of convergence in knowledge
  1000. role and nature of paradigms in knowledge, both paradigms as knowledge and the process of paradigms
  1001. role and nature of parody in knowledge
  1002. is the simple stuff really simple, or is that an illusion and getting the simple stuff really, really right is the key?
  1003. is knowledge really exploding, or are our metrics and tools just really bad?
  1004. communities based on specific scenarios, common experiences, and specific needs
  1005. knowledge of common experiences, or even uncommon experiences
  1006. James Burke’s Knowledge Web (Connections) — how can some of this knowledge be accessed and manipulated?
  1007. social patterns as forms of knowledge
  1008. emergent knowledge — how to treat it in contrast with established knowledge or accepted knowledge
  1009. implicit semantics
  1010. factoring in cost of errors when considering relevant knowledge
  1011. cultural knowledge
  1012. folk ontologies — make it a real concept — how informal communities develop ontologies
  1013. trends and fashions and fads — transient knowledge
  1014. semiotics — syntax + semantics + pragmatics (Eco, Peirce)
  1015. meaning to the individual vs. meaning to the group or community
  1016. cultural semantics
  1017. ambient semantics
  1018. rhythms as forms of knowledge
  1019. relationships and distinctions between interests and intentions
  1020. knowledge processes as a richer form of knowledge
  1021. summaries and summarization as forms of knowledge and knowledge processes
  1022. coping with the obnoxious
  1023. attention as a form of knowledge
  1024. RFID to gather knowledge from the consumer’s environment, allow the consumer to optionally and selectively “leave” a knowledge trail
  1025. make media (audio, images, video) object-based so that it can be knowledge-rich
  1026. interacting with intelligent objects
  1027. working with snippets of knowledge — what are they and how do they convey and signify knowledge
  1028. motivation: seeking truth vs. utility
  1029. addressing and moderating tedium
  1030. how to mesh curriculums, courses, lessons, tasks, and goals with raw knowledge, if that makes sense at all
  1031. expertise as a distinct form of knowledge
  1032. craft as a distinct form of knowledge
  1033. CCKW is *not* a teaching system or substitute for teaching — a lower-level tool for accessing raw knowledge
  1034. what is raw knowledge? bounds, limits, distinctions
  1035. H. G. Wells’ World Brain (1938)
  1036. relationships and distinctions between knowledge and ideas
  1037. coping with misconceptions and misunderstandings
  1038. coping with resistance to novelty or established practice or eclectic thinking or isolated thinking
  1039. the nature of being intrinsic
  1040. role of interpretation vs. clear facts
  1041. the raw power of the single mind vs. collective power — respective roles, relative value
  1042. role of simplicity contrasted with the utility of complexity
  1043. role of appreciation in accessing knowledge
  1044. knowledge as more of a process than an object
  1045. the need for Computer Science 2.0 to provide the conceptual basis for approaching knowledge webs
  1046. work on 5-page popular article on the CCKW, contrasting with current Web and Semantic Web, and other KW proposals
  1047. role of unconscious mental processes in knowledge processing — role of unawareness or unconscious awareness
  1048. the disconnect between how humans normally interact and how computer systems present information and interact with humans
  1049. emotional states and expressions as knowledge
  1050. psychological issues related to knowledge processing
  1051. coping with cruelty and bullying in knowledge
  1052. the quest for fundamental explanations
  1053. unconventional experiences
  1054. knowledge competition
  1055. epistemology: the nature and grounds of knowledge, the study of the nature and foundations of knowledge
  1056. issues of intellectual power
  1057. cooperative venture: consumers and their agents and the agents of other consumers and those other consumers
  1058. knowledge related to consumer commercial transactions
  1059. predictive knowledge
  1060. knowledge about causation, cause and effect, contrast with coincidence and correlation
  1061. the nature of inference, how to segue to other techniques
  1062. computational approaches that make real sense for knowledge processing
  1063. what is a knowledge processor? (ala word processor?)
  1064. connecting to your peers: using knowledge to locate your peers
  1065. knowledge about “my projects”: sharing and finding potential collaborators
  1066. what does it really mean to be “well-informed”?
  1067. introspective knowledge, conscious awareness
  1068. sensing when you are “in the dark”
  1069. barriers to knowledge
  1070. frontiers of knowledge
  1071. what is the analogy for knowledge to the physics concept of a “field”? — “patterns of order”
  1072. model of knowledge processing that reflects both Darwin (evolution) and Einstein (relativity) — what else?
  1073. focus on knowledge as a process (knowledge processing) rather than as an object — metaphor of watching a movie
  1074. CCKW is really a “knowledge processing environment”
  1075. regularities: are they really rules or potentially transient forms of knowledge?
  1076. unifying the view of knowledge with communication and signification
  1077. role of myths in knowledge
  1078. role of rhetoric in knowledge, combined roles of rhetoric and ideology and mythology
  1079. are narratives and stories the same concepts or are they only partially related and somewhat distinct?
  1080. forms of knowledge that are symbols far beyond the raw knowledge itself (e.g., well-known books and treatises)
  1081. how to incorporate books into the CCKW, especially wrt IP issues (copyright)
  1082. morality as yet another form of knowledge, coping with the morality vs. truth issue
  1083. harsh reality: there is no “truth” per se in a global CCKW
  1084. need to accept and cope with “the enemies of science”
  1085. criticism as a form of knowledge
  1086. refutation as a form of knowledge
  1087. aesthetics as a distinct form of knowledge
  1088. coping with apparent “formula” that mask complexity or inconsistencies
  1089. defining a position or interest based on questions rather than simply asserting facts — may be a lot easier and much more honest
  1090. is their an “invisible hand” that guides the “market” of knowledge processing?
  1091. scams and hoaxes and frauds and cons as forms of knowledge
  1092. coping with “folk wisdom”
  1093. are the distinctions between qualitative knowledge and quantitative knowledge as clear as they are made out to be?
  1094. characterizing “defining differences”
  1095. characterizing “to what degree”
  1096. coping with blurring of boundaries
  1097. characterizing (alleged) continuums and smooth distributions
  1098. drawing lines: issues, coping, facilitating, managing
  1099. rationales
  1100. supporting both specialization and generalization, but “specialized” support for both
  1101. differences as a distinct form of knowledge
  1102. need a small set of knowledge processing operators to master all of this complexity
  1103. organizing principles: for knowledge overall and as a form of knowledge
  1104. entanglement of knowledge
  1105. what does anyone really know? — what we think we know vs. what we actually know
  1106. role of cognitive dissonance in knowledge
  1107. specific knowledge processing capabilities focused on facilitating cooperation
  1108. role of “a theory of everything”
  1109. concept of “relevantly similar” when matching concepts — observer-specific concepts
  1110. representing and processing visual knowledge
  1111. mind-independent truth — pros and cons
  1112. significance of clever explanations
  1113. role of common threads in knowledge processing
  1114. framing and re-framing knowledge, meta-framing
  1115. coping with manipulation and even brainwashing
  1116. facilitate counterintuitive thought
  1117. coping with unintended consequences
  1118. role of chaos theory in knowledge processing
  1119. is too much knowledge an inherently bad idea? is there really a concept of knowledge overload?
  1120. dynamic ontologies are essential — the world is constantly changing, emergence is a real factor
  1121. economies: a form of community with rules, authorities, methods, opportunities, and specialized knowledge
  1122. smart things and ubiquitous smart things — a whole new level of forms of knowledge and knowledge processing
  1123. knowledge and knowledge processing related to the boundary between the real and cyber worlds
  1124. acknowledging and coping with “the limits of science”
  1125. coping with “knowledge” that is neither measurable nor predictable
  1126. codifying “what matters”
  1127. coping with “fashionable certainties”
  1128. knowledge as a “force”
  1129. spiritual knowledge — unique requirements?
  1130. collective consciousness: distinct forms of knowledge
  1131. shards of knowledge
  1132. “the tree of knowledge”: what is it, really?
  1133. audience-friendly knowledge: pros and cons
  1134. emotional intelligence
  1135. time: what do we really know about it?
  1136. strict distinctions: how strict are they and how distinct are they?
  1137. the distinction between reality and fiction: objective and subjective difficulties
  1138. outrage: aspects that are a form of knowledge
  1139. defense of ideas as a distinct form of knowledge
  1140. is it really “time” to pursue the consumer-centric knowledge web, or is even the research still beyond our intermediate-term grasp?
  1141. coping with distortion and exploitation
  1142. welcome and unwelcome knowledge
  1143. grand claims and wild promises
  1144. value-free knowledge: pros and cons
  1145. role of passion in knowledge and knowledge processing
  1146. role of articulation in knowledge processing
  1147. role of “unlearning” in pursuit of knowledge
  1148. “semantic annotation” is *not* a user-friendly approach to producing a knowledge web
  1149. role of multiple “angles of attack” on knowledge, points of view
  1150. important knowledge: what are the most important things to know? top 100? top 10? top?
  1151. what are the most important things that the CCKW can do for any consumer?
  1152. what fears can the CCKW help to curb?
  1153. heuristics as a distinct form of knowledge
  1154. fuzzy knowledge — a distant cousin of fuzzy logic
  1155. spam as a form of knowledge (e.g., knowledge of scams)
  1156. assessing the value of knowledge — down to slight, zero, and even negative
  1157. distinguishing the cost and value of knowledge
  1158. dialogues as a component of knowledge
  1159. networks of relationships as a distinct form of knowledge
  1160. seeking knowledge (apart from a specific need) as a distinct knowledge processing activity
  1161. the nature of expanding a knowledge web
  1162. what is the nature of the distinction between that which is knowledge and that which is not knowledge?
  1163. non-knowledge: a dead-end, terminal piece of information which cannot be further interpreted as knowledge
  1164. artificial distinctions as a distinct form of knowledge
  1165. continuity of knowledge (e.g., people retire and take their knowledge with them)
  1166. “Email is where knowledge goes to die” — great source for knowledge mining
  1167. meetings: big challenge representing the “group” knowledge expressed in a typical meeting
  1168. the world of statements: how is knowledge expressed?
  1169. the origin of knowledge: what was the first knowledge?
  1170. the nature of compounding of knowledge
  1171. OWL — how much of it can be reused or used as a foundation for a richer and more robust knowledge platform?
  1172. an “assembly language” for knowledge vs. a user-oriented higher-level language — need both, and of course the runtime support
  1173. an interpreter for the knowledge platform vs. an optimized compiler for specialized, high-performance apps — need both
  1174. distinguish casual knowledge from power knowledge or automated language — many forms of language, not one size fits all
  1175. need a new term for “machine-processable knowledge” — automated knowledge? machine-analyzed knowledge?
  1176. state of the art: keyword text and tags (metadata)
  1177. “The central idea of the Semantic Web is to extend the current human-readable web by encoding some of the semantics of resources in a machine-processable form.” — the central idea of a true Knowledge Web is to encode *all* of the meaning of *all* resources
  1178. what applications will be *possible* with a knowledge web that would *not* be economically feasible with the Web or even the Semantic Web?
  1179. true reuse of knowledge: use by others for applications not foreseen by the creator of the knowledge
  1180. concept of an “open world”: knowledge is never “complete”
  1181. law: interesting challenge since lawyers and courts have a highly-technical view of law, but consumers need access to some amount of that knowledge
  1182. automatically connecting isolated pockets of knowledge
  1183. reconciling differences in terminology — both syntactic and semantic differences
  1184. mappings are distinct forms of knowledge
  1185. “A knowledge web is a set of interconnected statements that form a coherent body of belief and that are related according to a set of rules or principles appropriate to a domain.”
  1186. role of mathematical rigor and formal analysis in knowledge processing: pros and cons — level of research needed
  1187. browsing knowledge: need innovative approaches — more research — partially collapsible 3-D “outline”
  1188. analogy to an outline processor for knowledge webs — with sophisticated collapsing capabilities
  1189. “pocket guides” for any area of knowledge as a starting point for searching
  1190. original motivation for this paper: people have focused on IT apps for software agents, but how can agent technology be best applied to the consumer domain? Answer: a knowledge infrastructure that both agents and consumers can relate to in a deep sense
  1191. example: capturing “Intelligent Design” as knowledge, regardless of whether you “believe” in it
  1192. facts: knowledge is much more than a collection of facts
  1193. let a thousand user interfaces bloom — no idea which types of UI will really give us quantum leaps forward
  1194. nature of situated knowledge
  1195. key challenge: consumers tend to be quite fickle and finicky — the CCKW should *exploit* those characteristics
  1196. support mechanisms for active listening — reinforce knowledge for both the writer and the reader
  1197. distinction between a “concept web” and a knowledge web — knowledge is more than simply a network of concepts
  1198. idea: a simple “concept audit” of a user’s content — look up a large dictionary of terms and topics and produce a usage map or “cloud”
  1199. various interpretations of the term “knowledge web” — even informal, loose-text, and old-fashioned hyper-linked Web pages
  1200. lessons learned from Wikipedia
  1201. relation of standard topic maps to an overall knowledge model
  1202. Project Halo — Digital Aristotle — Paul Allen, Vulcan Inc.
  1203. utility of using geometry for representing complex knowledge
  1204. making knowledge more accessible: goal: making knowledge immediately/instantly accessible
  1205. exploit the synergy between webs of knowledge and webs of people
  1206. uncovering hidden linkages or connections or relations
  1207. meaning of “core knowledge” — variety of cores
  1208. nature of “media” and its interaction with knowledge and knowledge processing
  1209. “knowledge links” or “semantic links” or “semantically meaningful links” vs. hyperlinks — labels, attributes, concept/meaning etc.
  1210. knowledge acquisition processes as a form of knowledge
  1211. description of processes vs. “facts”
  1212. distinctions between structure and content
  1213. federated knowledge
  1214. overlay networks for the knowledge web to enable selectivity
  1215. using the knowledge web to facilitate cross-fertilization and building bridges between communities
  1216. a knowledge discovery scripting language
  1217. knowledge web as a digital brain — no a great analogy, but may have value for popularization
  1218. role of history in knowledge processing
  1219. support a variety of discovery modes, ranging from rapid scan to fine-tooth detail
  1220. acknowledge that the best we can ever do will always be only an approximation of true knowledge
  1221. case: why has the Open Directory Project (DMOZ) failed so miserably when it had such great promise
  1222. themes as a distinct form of knowledge
  1223. nature of knowledge life cycles
  1224. logging knowledge
  1225. role of thriving in chaos to knowledge processing
  1226. role of biography in knowledge transfer
  1227. the distinctions between “what we know”, what is in our minds, and how we represent in the real world “what we know”
  1228. role of conflation — pros and cons
  1229. knowledge structures of the human mind vs. computational knowledge structures
  1230. mental knowledge: knowledge as it exists in human minds
  1231. media knowledge: knowledge as it exists in physical media, including speech
  1232. computational knowledge: knowledge as it exists within computers and is transmitted across computer networks, including decoding of meaning
  1233. knowledge vs. reality vs. perception
  1234. role of understanding in knowledge and knowledge processing
  1235. “how to” as a distinct form of knowledge
  1236. the spread of ideas as a phenomenon to be studied and promoted
  1237. knowledge as an ecology (knowledge ecology) rather than simply a process or artifacts — actually, many overlapping knowledge ecologies
  1238. body of knowledge: what defines the boundaries
  1239. consumer information: what vendors and marketers crave
  1240. visible knowledge, knowledge visualization
  1241. zero knowledge: ala privacy and security
  1242. role of education resources to consumer knowledge
  1243. methods of coping with missing or fragmentary data
  1244. indigenous knowledge or local knowledge: unique to a given culture or society
  1245. role of ego in knowledge: reality and coping and avoiding
  1246. machine-decodable knowledge: complements “encoding” of knowledge
  1247. Knowledge Management does not have a beginning and an end. It is ongoing, organic, and ever-evolving.
  1248. a consumer’s personal knowledge assets or knowledge wealth
  1249. uncovering hidden knowledge
  1250. communities of gossip: acknowledging their existence and coping with them
  1251. MySpace and Facebook: lessons learned
  1252. short article: identity management, privacy, and identity-shielded content
  1253. best practices for knowledge processing
  1254. knowledge in the small vs. knowledge in the large
  1255. personal knowledge management (PKM)
  1256. community memory
  1257. example app: nutrition information
  1258. example app: folk medicine: pros and cons
  1259. resources as a distinct form of knowledge
  1260. federated knowledge, knowledge federations, federated knowledge web
  1261. “herding” as a metaphor for knowledge processing
  1262. KAT — Knowledge and Agent Technology
  1263. consumer-oriented: the consumer is in the loop
  1264. consumer-centric: the consumer controls the loop
  1265. example app: “products I’d like to buy”
  1266. knowledge fragments as a preferred “unit” for expressing knowledge
  1267. knowledge as a snapshot: emphasize the open-ended, dynamic nature of knowledge
  1268. what is the knowledge analogy to land — the ultimate foundation
  1269. the nature of holistic views of knowledge
  1270. role of taxonomies in facilitating the sharing of knowledge
  1271. nature of knowledge repositories
  1272. how are we all connected? the many mechanisms
  1273. dictionary stacks: common terms as foundation blocks, layers of ever-finer specialization and personalization
  1274. core truths
  1275. knowledge ecosystem mapping
  1276. personal inquiry
  1277. decision support systems (DSS) — from a consumer perspective
  1278. knowledge inventory
  1279. knowledge-intensive activities
  1280. knowledge continuity management
  1281. the complementary nature of the intuitive and the rational
  1282. knowledge divides
  1283. knowledge transformation
  1284. social dimensions of knowledge
  1285. knowledge leaders, knowledge leadership
  1286. vital knowledge
  1287. central question: to what extent can we exploit the field of knowledge management as a basis for forming the consumer-centric knowledge web?
  1288. examples and counter-examples as distinct forms of knowledge
  1289. utility of knowledge programming (LP)
  1290. approaches to modeling as distinct forms of knowledge
  1291. communication of hypotheses
  1292. beyond DNS: new ways to communicate location on the Internet in the context of the CCKW
  1293. beyond vendor-controlled servers: where and how is information stored in a way that is controlled by consumers
  1294. define consumer: not simply producer vs. consumer, but “the people”
  1295. federated ontologies: stitching and compensating for misaligned concepts, even for seemingly simple concepts
  1296. CCKW needs to emphasize at the executive level that it is about machine-understandable knowledge as opposed to knowledge represented primarily as text or metadata or tags
  1297. a research narrative as a distinct form of knowledge
  1298. system of thought as an aggregate “unit” of knowledge — what does it really mean?
  1299. layers of knowledge — what does it really mean to “layer” knowledge?
  1300. knowledge-centric view of organizations and systems — is this reasonable, or is it a myth and systems need to shift towards being consumer-centric?
  1301. knowledge integration: this is the big problem, especially with the Semantic Web, but also the big opportunity
  1302. ontological alignment: this is the main technical problem behind knowledge integration
  1303. roles of “knowledge components” such as judgment, design, leadership, better decisions, persuasiveness, wit, innovation, aesthetics, and humor in knowledge — knowledge-intensive skills, less-digitized factors — support for them [Prusak, 2001]
  1304. organizations that know how to do things [Winter 1993]
  1305. knowledge: what’s left after you remove all of the raw data and structured information
  1306. distinctions and synergies between narratives and conversations
  1307. from a practical perspective, how can consumers validate knowledge?
  1308. distinctions and synergies between the worlds of reality and theory
  1309. rhetorical convenience: balancing between coping with it and facilitating it
  1310. role of salience in knowledge
  1311. role of learning-strategies and learning strategies, both in capturing knowledge and discovering knowledge
  1312. perspectives of psychology for capture, storage, dissemination, and discovery of knowledge
  1313. perspectives of economics for capture, storage, dissemination, and discovery of knowledge
  1314. the nature and role of mastery of knowledge
  1315. role of “social facts” in knowledge [Durkheim, 1982]
  1316. distinctions and relationships between “know how” and “know what”
  1317. what does it mean for knowledge to be “digitized”? Is there a better way, an alternative to “digitizing”?
  1318. data feeds as distinct forms of knowledge — e.g., relationship between the feed contents and the actives processes behind the feed
  1319. knowledge processing: initial perception, mental processing, recording, communicating, perceiving (the recording), assimilating
  1320. role of memes in knowledge processing
  1321. what are the different kinds of knowing?
  1322. roles of will and motivation in knowledge processing
  1323. obstacles to broad-scale knowledge processing imposed by tacit knowledge
  1324. forms of selectivity as distinct forms of knowledge
  1325. the knowledge flood, the knowledge ocean
  1326. natural cognitive processes, natural knowledge processing
  1327. human factors at the knowledge level, as opposed to at the user-interface level
  1328. programming software agents with the knowledge of how to figure out how to learn, as opposed to pre-programming them with specific learning faculties
  1329. identity: successor to name and email address, including partial and selective anonymity
  1330. software agent technology as an “API” for consumers and their knowledge and services
  1331. where will a consumer’s software agents “be”? Answer: everywhere, but nowhere in particular.
  1332. what is the nature of a preference?
  1333. implications of/for CCKW for/of the very young, the very old, the very disabled, and the very different
  1334. coping with the wide varieties of knowledge comprising a plan or strategy or even an approach — e.g., facts vs. expectations and hypotheticals
  1335. relationship between human capital and knowledge, value of promoting human capital in pursuit of getting greater value from knowledge
  1336. enhancing individual effectiveness
  1337. adding “game mechanics” to knowledge processing to make it more appealing
  1338. the nature of cliques: what are the incentives and disincentives, impacts, coping
  1339. roles of core groups in knowledge processing, both creation and assimilation
  1340. plumbing: the aspects of the CCKW infrastructure that are strictly distinct from any knowledge content
  1341. knowledge of norms and normative knowledge
  1342. digital lifestyle aggregator (DLA) — seems overly complex and cumbersome and unlikely without a true knowledge infrastructure
  1343. Lifestreams as a metaphor and model for organizing much of a consumer’s knowledge [Gelernter]
  1344. conversion between tacit and explicit knowledge (T-E, E-T, E-E, T-T) [Marwick, 2001]
  1345. the tacit dimension of knowledge [Polanyi, 1996/1997]
  1346. data vs. speculation — bases for knowledge claims
  1347. cognitive frameworks
  1348. the nature of expertise vs. tacit knowledge
  1349. problem-solving and troubleshooting as distinct forms of knowledge
  1350. specification of problems and scenarios as distinct forms of knowledge
  1351. global virtual bit storage network as a key infrastructure component, plus the robust network to access the bits
  1352. metrics for assessing the health of a virtual bit (e.g., degree of redundant storage, multiple access paths, access time, degree of privacy, etc.)
  1353. distinguish privacy of content vs. privacy of ownership vs. privacy of identity
  1354. add a slide to the PPT presentation on the nature of knowledge: mental, tacit, explicit, information, artifact, processing, etc.
  1355. role of complexity theory in knowledge processing
  1356. emphasize role of complex adaptive systems (CAS) in knowledge processing
  1357. knowledge of complex adaptive systems (CAS) as a distinct form of knowledge or meta-knowledge
  1358. the CCKW infrastructure needs to support complex adaptive systems (CAS) as an essential building block in knowledge processing — both manually setting up a customized CAS framework and pattern matching to recognize a CAS in operation and then provide support for it
  1359. can we ever really know what we know?
  1360. beyond search: what is explicit search a special case of? e.g., seeking connections and opportunities
  1361. role of anticipation and anticipatory influences in knowledge processing — e.g., role of beliefs about the future
  1362. nature of curiosity and its role in knowledge processing
  1363. nature and role of challenging assumptions in knowledge processing
  1364. nature and role of emotions in knowledge processing
  1365. nature and role of first movers in knowledge processing
  1366. Orwell’s 1984: will CCKW and other knowledge processing approaches deter or abet Orwell’s gloomy scenario
  1367. GPS (et al) position information: how will it affect knowledge processing
  1368. affect of work on consumer knowledge processing
  1369. roles of instant messaging, chat rooms, discussion forums, mailing lists, blogs, and podcasts in knowledge processing
  1370. relationship and distinctions between ability/skills/aptitude and knowledge — are the former synonyms for tacit knowledge?
  1371. unexpressed knowledge, inexpressible knowledge
  1372. are tacit knowledge and implicit knowledge synonymous? if not, clarify the distinctions
  1373. effectiveness of storytelling for conveying knowledge, but pros and cons for accuracy
  1374. what might the impact of CCKW be on “The Knowledge Economy”?
  1375. coping with rapidly changing knowledge
  1376. knowledge sharing: as an explicit activity vs. implicit based on the CCKW knowledge infrastructure
  1377. deterring and coping with corruption
  1378. nature of knowledge communities and how to facilitate and support them
  1379. debriefing: tacit knowledge download
  1380. media content explicitly constructed to convey knowledge — e.g., audio and video clips and visuals
  1381. exchanging (multi-directional interaction) as a method for transferring knowledge
  1382. the critical issues of identifying consumer needs and interests
  1383. the nature and roles of change agents and thought leaders — facilitating them, empowerment
  1384. how to package knowledge: wide variety of approaches, need for further innovation
  1385. the nature of informal knowledge networks: facilitating them
  1386. nature and role of newsletters for knowledge processing
  1387. nature of knowledge gaps and how they are filled
  1388. limited attention span: nature and coping
  1389. nature and role of illusion: pros and cons
  1390. add PPT slides: 1) What is Knowledge? and 2) What is a Software Agent?
  1391. need for a global storage grid for cheap, reliable, robust storage of knowledge virtual bits (knowledge bits)
  1392. incentives and disincentives for knowledge sharing: protecting valid disincentives while promoting valid incentives
  1393. impact of consumer culture on knowledge processing
  1394. nature and role of critical analysis in knowledge processing
  1395. nature and role of “the temple” vs. “the factory” in knowledge processing
  1396. quantifying qualitative knowledge: pros and cons
  1397. nature and role of bandwagons in knowledge processing
  1398. the nature of nature as contrasted with behavior
  1399. dissipation of knowledge: pros and cons, factors, attenuating
  1400. nature and role of induction in knowledge processing
  1401. nature and role of complex systems in knowledge processing
  1402. nature and role of emergent order and disorderly systems in knowledge processing
  1403. universals, range of any knowledge claim
  1404. adages as a distinct form of knowledge
  1405. stereotypes as a distinct form of knowledge
  1406. prophesy as a distinct form of knowledge
  1407. fairly comprehensive model of human nature as a key part of the CCKW knowledge infrastructure
  1408. nature and role of absolutes and absoluteness in knowledge processing
  1409. nature and role of personal morality in knowledge processing, building a personal morality knowledge structure
  1410. sympathy and empathy as distinct forms of knowledge
  1411. stimulants for knowledge processing: how to facilitate them
  1412. retardants for knowledge processing: how to cope with and deter them
  1413. how do groups learn (as opposed to individuals) and how is group knowledge different from individual knowledge
  1414. nature and role of constructive non-conformity and creative tension in accelerating knowledge processing
  1415. “the line”: demarcation between knowledge that is visible and apparent to the consumer vs. non-visible knowledge that is needed deeper in the knowledge infrastructure to support the consumer
  1416. CCK: Codified Consumer Knowledge
  1417. CIK: Codified Consumer Knowledge
  1418. CGK: Codified Group Knowledge or Codified General Knowledge
  1419. COK: Codified Organizational Knowledge
  1420. CUK: Codified Universal Knowledge
  1421. the knowledge claim as the key building block, the raw form of knowledge accepted as input to knowledge processing
  1422. key goal of CCKW: eliminate the concept of search
  1423. nature of distinctions between knowledge processing for consumers under CCKW and the consumer’s software agents under CCKW
  1424. nature of supply and demand for knowledge processing
  1425. nature of effective distribution of knowledge: facilitate it and measure it
  1426. nature and role of mental models for knowledge processing
  1427. support for identifying and coping with “worst ideas”
  1428. nature and role of centrality: relative centers? relationship to context
  1429. nature and role of human nature in knowledge processing — varying expression and influence
  1430. nature and role of conceptual models in knowledge processing
  1431. nature and role of entity identity — how do we ground references to things
  1432. what should the characteristics of a robust “knowledge scenario” be? what impression should it leave?
  1433. nature and role of thesauri in knowledge processing
  1434. nature and role of confluence in knowledge processing — events, forces, motivations, influences, priorities
  1435. if a picture is worth a thousand words, hw can we access the meaning of those words?
  1436. web of meaning: original intent of Semantic Web, CCKW depends on it
  1437. nature and role of feedback loops in knowledge processing
  1438. nature and role of natural knowledge processes and rules in knowledge processing
  1439. nature and role of goal sharing in knowledge processing
  1440. nature and role of learning events in knowledge processing
  1441. rule sets as a form for knowledge
  1442. nature and role of declarative knowledge and procedural knowledge
  1443. nature and role of knowledge as a response to stimuli in knowledge processing
  1444. nature and role of knowledge production in knowledge processing
  1445. interoperability of knowledge, especially with ontologies which are not strictly aligned
  1446. knowledgeset
  1447. nature and role of abstract concepts in knowledge processing
  1448. nature and role of making sense in knowledge processing
  1449. nature and role of learning and adapting in knowledge processing
  1450. nature and role of coaching in knowledge processing
  1451. nature and role of flames as distinct forms of knowledge
  1452. nature and role of the three-world Popper knowledge model: World 1: physical world (real objects and phenomena), World 2: mental or psychological world (perception), and World 3: products of the human mind (language and engineered objects)
  1453. nature and role of simulation and modeling results and forecasts in knowledge processing
  1454. constructing a science-based model of CCKW, including economics
  1455. nature and role of anticipatory attention in knowledge processing
  1456. nature and role of metadata — distinction from meaning of content itself
  1457. nature and role of interpretation in knowledge processing
  1458. nature and role of spimes in knowledge processing
  1459. how do we test knowledge representations for their fidelity and comprehensibility?
  1460. nature and role of gestures, facial expressions, and body language in knowledge processing
  1461. nature and role of evocative knowledge objects (EKOs) in knowledge processing
  1462. nature and role of theory objects in knowledge processing
  1463. difficulties and strategies for coping with ambiguities concerning abstraction and instances
  1464. distinguishing and contrasting meaning for a knowledge web and inference for the Semantic Web — “the phrase ‘semantic web’ makes it sound as if meaning is somehow critical to our enterprise. It is _not_. Our central problem is _inference_.” — Drew McDermott, Yale University, Computer Science Department
  1465. still need to refine definition of knowledge
  1466. nature and role of hunches in knowledge processing
  1467. using software agents to transcend complexity
  1468. nature and role of the attention economy in knowledge processing
  1469. nature and role of writing styles and linguistic patterns in knowledge processing
  1470. nature and role of multiple levels of meaning in knowledge processing
  1471. nature and role of reader-specific contextual meaning in knowledge processing
  1472. nature and role of private vocabularies in knowledge processing
  1473. what can be gleaned from the FOAF Project (Friend Of A Friend)
  1474. nature and role of the open-world assumption in knowledge processing
  1475. nature and role of any inherent limitations of fidelity of representation of the real world in knowledge processing — not just the real world
  1476. emphasize fundamental research
  1477. nature and role of gaming and story-telling in knowledge processing — exploiting their full potential
  1478. what might the topology of a knowledge web really look like? how can it be visualized?
  1479. how to visualize knowledge relationships
  1480. contrast a knowledge agent with an intelligent agent — KA needs to be able to work with knowledge, but not at the human intelligence level of competence
  1481. knowledge networking: what does the term really mean?
  1482. nature and role of BrainJams in knowledge processing
  1483. nature and role of Chautauquas in knowledge processing
  1484. nature and role of knowledge silos in knowledge processing
  1485. nature and role of the metaphor of the blind men and the elephant in knowledge processing
  1486. approaches to reducing complexity
  1487. knowledge schemas — extend from the information domain (since W3C has bastardized the concept of ontology anyway)
  1488. community knowledge sharing and community knowledge systems (CKS) [Bobrow@PARC]
  1489. sensemaking [PARC]
  1490. nature and role of basic truths of nature in knowledge processing
  1491. solving problems vs. coping with them
  1492. grounded concept: conceptualize from data or grounded in data
  1493. concept recognition: from data
  1494. groundable concepts
  1495. supporting concepts
  1496. ontology: concepts, relations, and instances
  1497. nature and role of topic ontology in knowledge processing
  1498. relation of natural language grammar to knowledge
  1499. structure of natural language
  1500. social value of ambiguity
  1501. nature and role of addressing overall consumer social needs in knowledge processing
  1502. detail the ways in which Semantic Web is too weak for consumer-centric knowledge
  1503. tacit understanding vs. tacit knowledge
  1504. support mechanism for thinking, reasoning, understanding, challenging
  1505. what aspects do we need to be cautious about, such as “the line” between computational and human intelligence and knowledge
  1506. grade-level for knowledge: judging it, transforming to it
  1507. nature and role of personalization — move to new levels (e.g., software agents to effect personalization)
  1508. coping with skew and bias in searches
  1509. nature and role of the distinctions between search and lookup
  1510. CCKW as a distributed alternative to traditional search engines
  1511. what is the unit of ignorance?
  1512. nature of questions — role and as knowledge
  1513. nature and role of numbers as knowledge
  1514. mathematics and formulas as knowledge
  1515. nature of being “lost”
  1516. nature of the relation to the real world
  1517. practical knowledge vs. theory knowledge
  1518. structured natural language, pseudo natural language, pidgin natural language
  1519. beyond direct access and abstract names — how to refer to things
  1520. nature and role of pre-conditions and constraints
  1521. expressing probabilistic qualities of knowledge
  1522. faux banana example — nature of “like”
  1523. nature and role of serendipity
  1524. hard knowledge vs. soft knowledge
  1525. soft vs. semi-hard knowledge
  1526. software agents as “reach extenders”
  1527. nature and role of creating new terms
  1528. email: knowledge that is hibernating, stunned, at rest, resting
  1529. knowledge “at rest” vs. form of intellectual energy
  1530. tiered knowledge (levels)
  1531. downsides and dangers of systemization
  1532. the world is a mountainous jungle (and swamps and deserts and oceans) — not “flat” — stratification, silos
  1533. value of diffuse knowledge
  1534. essence of narrative — a key of knowledge
  1535. nature and role of linking in narrative
  1536. knowledge as a system — what is a system?
  1537. cultural alignment
  1538. Add to PPT: benefits to consumer
  1539. middleware: “The Matrix”
  1540. look for an ultra-simple disruptive angle
  1541. culture-shift knowledge
  1542. app: health-care delivery
  1543. look for lifestyle angles
  1544. the needs and interests of Millennials: information, entertainment, and social
  1545. email: knowledge that is dying?
  1546. stripes — disciplines, interests
  1547. 5-D virtual environment for knowledge experience vs. 7-D or 12-D — what are the 5th and 6th dimensions
  1548. consumers bring technology into work
  1549. the consumer almighty
  1550. nature of system vs. community
  1551. nature and role of walled gardens
  1552. software agents: psychology, social computing — moods — coping, representing, analyzing
  1553. evolutionary learning
  1554. evolutionary knowledge processing, construction
  1555. basic map, geography, and geology knowledge
  1556. basic geometry and spatial relationship knowledge
  1557. GPS and RFID knowledge — as context and as knowledge itself
  1558. basic general history and chronology knowledge
  1559. basic government knowledge
  1560. basic business knowledge
  1561. basic biology, health-oriented, nutrition, and medical knowledge
  1562. “best pizza” metaphor — difficulties
  1563. knowledge requests vs. Q&A — research
  1564. animated knowledge
  1565. incremental disclosure knowledge
  1566. quizzes as knowledge
  1567. need a range of knowledge languages
  1568. need a range of knowledge packaging
  1569. imperatives as knowledge — who, where, and when
  1570. nature and role of junk knowledge
  1571. nature and role of subject vs. object — objective knowledge
  1572. methods for “folding” knowledge
  1573. GALS applied to knowledge
  1574. Knowledge Factor
  1575. what is a consumer really?
  1576. word association as knowledge processing
  1577. UML for knowledge modeling
  1578. nature and role of “orbiting” in knowledge processing
  1579. nature of acquiring knowledge
  1580. consider a health focus for knowledge
  1581. nature and role of placeholder objects
  1582. nature of knowledge that solves a simple problem well
  1583. relationships as knowledge
  1584. concept aid
  1585. nature and role of utopias
  1586. collaborative environments
  1587. icons and symbols as knowledge
  1588. consider a roadmap “plan”
  1589. elaborate on the Global Virtual Storage Network (extract from DVPC)
  1590. PPT: emphasize why the consumer first
  1591. PPT: consumer owns and controls their own knowledge
  1592. nature and roles of levels of intelligence
  1593. contemplate a “spreadsheet” for knowledge — Knowledge 123
  1594. nature and role of spatial knowledge
  1595. nature and role of temporal knowledge
  1596. manifesto and requirements for a knowledge language
  1597. list forms of knowledge — how open-ended is it
  1598. list levels of knowledge — how open-ended is it
  1599. list levels of expertness (relative to assumptions) — how open-ended is it
  1600. need to address the basics of knowledge processing
  1601. how to promote knowledge processing
  1602. Latin as a neutral, common base for natural language knowledge processing — extended (modernized) Latin
  1603. how to process knowledge represented in a non-Latin language
  1604. future of “matching” — difficulty of alignment
  1605. consider a pseudo-natural language for knowledge processing — not fully processable by machine yet, but “ready” for future AI software
  1606. knowledge “depth” roadmap — text, soft, hard, flexible — “forms” of knowledge
  1607. veneer of formalism — limits and benefits
  1608. nature and role of anthropology in knowledge processing
  1609. “The most important questions of life have never been and probably never will be formalized.”
  1610. intuitive pragmatics
  1611. VivoMind — John Sowa
  1612. attention — belief — intention
  1613. AI winter
  1614. Internet Business Logic — executable open vocabulary English — www.reengineeringllc.com
  1615. cost of ownership for knowledge
  1616. cost of maintenance for knowledge
  1617. meanings: extensional, intensional, pragmatic, and modal
  1618. semiotic web vs. knowledge web vs. Semantic Web
  1619. nature and role of ontological engineering — for fixed knowledge structures
  1620. “relevant literature”
  1621. SUO — Standard Upper Ontologies
  1622. knowledge process for “the mobile world”
  1623. philosophy web
  1624. is plot related to meaning?
  1625. Penrose
  1626. ISO Common Logic
  1627. Sowa FMF modules/components
  1628. CLCE — Common Logic Controlled English
  1629. nature and role of schools of thought
  1630. USECS — substances and processes
  1631. transfer learning
  1632. reinforcement learning
  1633. learning by example
  1634. how knowledge can be structured
  1635. school-independent knowledge models
  1636. broader view of mechanisms for knowledge transfer
  1637. nature and role of consumers as agents
  1638. semantic grounded data sharing
  1639. consider CCKW focused on hand-held mobile
  1640. Machine Learning Technologies
  1641. what are the issues for natural language processing?
  1642. nature and role of feedback loops
  1643. nature and role of proposals
  1644. ontology reconciliation
  1645. what is a better term for “ontology” as a specification for a domain?
  1646. nature and role of propagation
  1647. knowledge has pragmatics
  1648. collaborative knowledge acquisition
  1649. nature and role of state space
  1650. the data exchange problem — distributed data
  1651. agent management
  1652. Amazon.com as a knowledge model
  1653. Quine (1981) Theories and Things
  1654. vaguer concepts which convey real meaning my virtue of common usage vs. pseudo-precise concepts
  1655. nature and rol of intended model
  1656. 43things as a social knowledge structuring mechanism
  1657. DOLCE: Descriptive Ontology for Linguistic and Cognitive Engineering
  1658. UFO: Universal Foundation Ontology or Unified Foundation Ontology — Harry Halpin, David Price
  1659. hard facts vs. mushy text vs. application/domain-specific data packet
  1660. ULO: Upper-Level Ontology
  1661. bottom-up/emergent and lower-level foundation ontologies
  1662. general meaning vs. specific meaning vs. contextual meaning
  1663. nature and role of “reaching” — understanding, communicating
  1664. learning through osmosis — simply being there and being attentive
  1665. coherence vs. novelty — emergence
  1666. “Ontology is composed of knowledge.”
  1667. create a new natural language comprehensible to both people and computers
  1668. a system and language analogous to LOGO but for traversing knowledge structures
  1669. is language the issue or is context the issue?
  1670. mental structures — need a mental structures model for computers that is compatible with the mental structures of the human mind, then many languages will do
  1671. nature and role of bewilderment
  1672. information overload caused by insufficient support for information selection, organization, and collaboration
  1673. beyond P2P
  1674. discovering promising partners, pool of cooperating partners
  1675. nature and role of shallow vocabularies
  1676. difficulties of manual translation, opportunities and benefits and limitations of semi-automated transformation
  1677. nature and role of incompatible semantics
  1678. IP: ontology vs. meaning
  1679. nature and role of which information one believes
  1680. nature and role of consequences of knowledge
  1681. nature and role of commitment to particular knowledge
  1682. emphasize evolution in knowledge and knowledge processing
  1683. intelligence vs. knowledge processing

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