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.)
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”.
- 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
- Executive Summary
- PowerPoint Presentation
- Short Article on Enabling Consumer-Centric Knowledge-Based Computing
- A Modest Beginning
- An Immodest Clamor
- Why Start With Consumers?
- What do Users Want and Need?
- Consumer Problems to be Addressed
- Consumer-Centric Rather than Merely Consumer-Oriented
- Key Aspects of Consumer-Centric Computing
- What is Knowledge-Based Computing?
- Doesn’t the Semantic Web Do All of This Already?
- What is a Software Agent?
- Intelligent Agents
- Multi-Agent Systems
- Software Agents: Helping Consumers with Coping and Facilitating
- Knowledge-Based Software Agent Technology
- Consumer Agent Vision and Consumer Knowledge Agent Vision
- Why is Software Agent Technology Required?
- Trend from Software to Services to Software Agents
- Artificial Intelligence, Machine Intelligence, and Computational Intelligence
- Intelligence Augmentation
- Artificial Artificial Intelligence
- Multi-Mind or Group Mind
- Collective Intelligence
- Knowledgesphere and Consumer Knowledgesphere
- Coping with Information Overload
- Facilitate Common-Sense Reasoning
- Software Agents as Consumer Proxies
- Coping with Information Fragmentation and Specialization
- Need for Calm Technology
- Radically Simplify the Computer Vocabulary Needed by Consumers
- Consumer Control and Controlling Authority
- Consumer Interests
- Overlay Networks
- Semantic Web
- Implicit Semantic Web
- Web Services
- Knowledge Web
- Consumer Knowledge Web
- Text Mining, Data Mining, and Knowledge Mining vs. Semantic Web
- Grid Computing
- Semantic Grid
- Centralized Applications are Bad; Distributed Applications are Good
- Centralized Databases are Bad; Distributed Databases are Good
- Network-Centric Applications vs. Global Knowledge-Centric Applications
- Software as a Service (SaaS) vs. Software as Agents
- Ant Paradigm
- Swarm Intelligence
- Analogies to Plants, Forces, and Chemical Agents
- Consumer Knowledge Web
- Consumer Knowledge Web 1.0
- Consumer Knowledge Web vs. Consumer Web
- Consumer/Agent Knowledge Web
- Infrastructure: Toolkits, Frameworks, Middleware, Platforms
- Interaction Machines vs. Turing Machines
- Organic Application Development
- Mash-Ups as an Application Model
- Autonomic Operation
- Asynchronous Operation
- Radically New View of “A Program” or “An Application”
- Macro Software Agents and Micro Software Agents
- Network Effects
- Social Computing
- Human Computing
- Tribes and Social Values
- Dynamic Coalitions
- Virtual Communities
- Mobs and Smart Mobs
- Online Democracy
- Reputation and Trust
- Ethics and Deontic Logic
- Psychology of the Consumer Knowledge Web
- Consumer Attitudes Towards Knowledge
- Consumer Attitudes Towards Software Agents
- Consumer Attitudes Towards the Consumer Knowledge Web
- On-Demand Knowledge vs. Never-Need-to-Demand Knowledge
- Direction and the Journey Itself, Not the Destination
- Persistent Storage of Consumer Knowledge
- Elimination of Indirect Personal Information
- Organizing Consumer Knowledge
- Ontologies, Taxonomies, Tagging, Tagsonomies, and Folksonomies
- Difficulties with Directories and Taxonomies
- Auto-Directory, Auto-Taxonomy
- MyLifeBits Lifetime Store
- Knowledge Evolution
- Knowledge Feeds: Web Feeds and “RSS” for Distributing Knowledge
- Software Agent Feeds
- Social User Interfaces
- Scalable: Transcending Scale
- Open Source Software
- Open Data
- Facilitate Entertainment
- What about Microsoft Bob?
- What About Ray Kurzweil’s “Singularity”?
- Consumer Use of Software Agents for Knowledge-Based Computing < 0.0001% of Kurzweil’s Singularity
- Computing Models
- Procedures vs. Tasks vs. Goals
- Values and Ideals and Life Goals
- Computing Infrastructure Will Vanish into Transparent Ubiquity
- Ultimate User Interface: Life Itself
- Retreat into the Background
- Location Awareness
- Ubiquitous Computing and Ambient Intelligence
- User Model of Software Agents
- More Deeply Satisfying than Toy-Like
- Toys, Games, Fun, and Play
- Potential Consumer Applications
- Potential Killer Apps
- Facilitate Barter
- GIS (Geographic Information System) Applications
- Architecture of Participation
- Consumer-to-Consumer Interactions (C2C)
- Consumer Networking and Social Networking
- Consumer Collaboration
- Implicit Collaboration
- Consumer Intelligence
- Life Mentor
- Life Agents
- Lifelong Learning
- Role in Education
- Facilitate Leadership
- Facilitate Creativity
- Facilitate Imagination
- Facilitate Dreaming and Aspiring
- Facilitate Natural Language Interfaces
- Overcome Language Barriers
- Opt-In is the Law
- Early Adopters: High-end (Professionals) and Low-end (Kids)
- Handheld Applications and Mobile Applications
- Mobile Environments
- Mobile-Agent Applications
- Management of Consumer Data
- DVPC: Distributed Virtual Personal Computer
- Virtual Networked Bits
- Sharing of Consumer Data, Information, and Knowledge
- Consumer-Centric File System — Consumer-Centric Knowledge Organizer
- Management of Medical Records
- Health, Nutrition, and Medical Applications
- Legal Applications, Management of Legal Information
- Coping With Uncertainty
- Facilitating Roles and Personae
- Learning to Learn
- Auto-Search, Intelligent Search Alerts and Notification
- Semantic Search: Deep Context vs. Simple Keywords
- Go to Google vs. My Agents are on It
- Searching vs. Sleuthing
- GMWIMW — Give Me What I Might Want
- Thinking Outside the Box
- Out of the Blue
- Peer-to-Peer (P2P), Agent-to-Agent (A2A) Metaphor
- Mass Customization
- Blogging and the Blogosphere
- Mobile-Phone Applications
- Fuzzy Logic
- Constraint Management to Automate Functions
- Psychology Applied to Software Agents
- Spam and Irrelevant Knowledge and Useless Information
- Identity and Anonymity
- Digital Identity
- Identity Theft
- Identity Union
- Big Brother
- Law Enforcement
- Information Warfare
- Levels of Autonomy
- Social Structures
- Consumer-Centric Tools
- Never a Need to Agree to “Trust Us”
- Human-Like Interface
- Intellectual Property: Enabler and Obstacle
- Legal Aspects
- Software Agents in Fiction
- What’s Next
- The Plan
- Where to Start
- Raw Notes and TBD
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.
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.
For a brief executive-level overview of the Consumer-Centric Knowledge Web, see the Consumer-Centric Knowledge Web PowerPoint presentation.
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.
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
- 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.
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.
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.
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.
Specifically, a consumer-centric knowledge web needs to fix the following problems:
- Search engines…
- Don’t know the meaning of the web pages that they crawl and index.
- Don’t know the meaning of the keywords used by the consumer.
- Don’t know the context of what the consumer is trying to accomplish.
- Consumer information is not kept private and under complete control of the consumer.
- Web is vendor-centric, rather than putting the consumer in absolute control.
- The Web and web sites and services don’t have even a basic comprehension of common sense concepts.
- Potential collaborators for consumers cannot be found easily.
- The Web (and Semantic Web) don’t do near enough to encourage broader and deeper consumer-to-consumer (C2C) interactions.
- Software agents can’t do a lot for consumers since they don’t know what the consumer is trying to do.
- Consumers can’t depend on dumb software agents.
- Consumers have no way to intelligently communicate with intelligent software agents.
- 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.
- The overall interface between consumers and vendors is too dumb.
- Consumers are unable to communicate with other consumers who do not speak their language.
- Mechanisms for expressing information are too primitive.
- Most Web interactions are task-oriented rather than permitting the consumer to express goals.
- 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.
- Web interfaces are quite noisy and distracting, rather than being based on calm technology.
- No easy way to enable the software agents of different consumers interact directly on behalf of all of those consumers.
- Consumers and software agents don’t have a common language for conveniently and comfortably communicating information, beliefs, desires, and intentions.
- No reliable mechanism for storing information and knowledge for consumers.
- 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.
- Simply put: The computer has no clue what the consumer means or what information means to the consumer.
- 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.
- 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.
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.
- Consumer is at the center and in control
- Calm Technology
- Automation is more important than highly interactive user interfaces
- Knowledge-based software agent technology
- Interaction with other consumers
- Maintaining privacy while enabling and encouraging sharing and collaborative behavior
- Network effects of consumer-to-consumer interactions
- Exploiting group and global knowledge
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.
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? 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.
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.
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 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.
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.
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.
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.
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.
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.
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 (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.
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.
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
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”.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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 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.
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.
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.
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.
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.
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 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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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 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.
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.
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 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.
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.
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.
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 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 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.
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.
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.
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.
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 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.
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.
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.
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.
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.
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.
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.
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.
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 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 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.
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.
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.
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
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.
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:
- The data formats are readily available and transparent.
- The data repositories themselves are accessible.
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.
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.
- 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.
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”
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.
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.
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.
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.
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.
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.
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.
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.
With advances in GPS and wireless networks, computer software within handheld devices can now tailor their behavior to the specific geographic location.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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
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.
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.
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.
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.
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.
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.
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.
Software agent technology can be used to generally assist in empowering consumers, helping them to identify opportunities for pursuing their interests.
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.
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.
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.
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.
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.
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.
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.
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.
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 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.
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.
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.
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.
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 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.
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 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.
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.
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.
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.
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.
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.
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.
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.
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.”
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.
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.
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.
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) 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.
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 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 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.
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 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.
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.
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.
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.
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.
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.
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 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.
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 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 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.
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.
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.
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 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.
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:
- Level 8 — independent entities which do not interact under any circumstances
- Level 7 — independent entities which sometimes interact of their own volition
- Level 6 — independent entities which sometimes interact out of an enforceable obligation or contract
- Level 5 — performance of specific, well-defined tasks in a synchronous manner for the user. Traditional computer software
- Level 4 — limited to delegation of specific, well-defined tasks that can be performed asynchronously for the user
- Level 3 — side-by-side, semi-supervised asynchronous operation in pursuit of one or more goals
- Level 2 — independent operation in pursuit of goals, as initiated by the user
- Level 1 — independent initiation of operations in pursuit of goals
- 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.
Consumer-oriented software agents will need significant awareness of the social fabric of which the consumer is a part, including:
- Citizenship (local, regional, national)
Knowledge of the consumers relationships can dramatically enhance a software agent’s ability to support the consumer
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.
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.
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.
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.
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.
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:
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.
- Robby the Robot in Forbidden Planet, 1956.
- The Robot in Lost in Space, 1965.
- 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.
- HAL, the self-aware computer, in 2001: A Space Odyssey by Arthur C. Clarke, 1968.
- Dixie Flatline in Neuromancer by William Gibson, 1984.
- Colin, the personal assistant, in Mona Lisa Overdrive by William Gibson, 1988.
- The Librarian in Snow Crash by Neal Stephenson, 1992.
- Agent Smith, et al in The Matrix movie series, The Matrix, 1999, The Matrix Reloaded, 2003, and The Matrix Revolutions, 2003.
- 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.
- 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.
- 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.
- 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:
- The capabilities are still way beyond current technology and depend on more dramatic advances in computational intelligence.
- The capabilities are more like dumb people than smart machines.
- The characters are rather two dimensional and not very rich.
- The focus is on human-like characters and simple tasks rather than on automating goals and reasoning. — ???
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.
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.
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.
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.
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.
- collective semantics
- a collective
- community building
- computer science research and development needed for foundation
- intelligent room, building, environment
- smart spaces
- contextual clues
- vendor-neutral apps
- minimize GI/GO
- information exchange
- facilitate targeted and desirable advertising
- consumer-oriented search engines
- multi-player games, MUDs
- search for meaning
- cooperation, competition, discovery
- community workspaces
- keeping consumer profile private (not centralized)
- monitoring personal security
- no more uploading
- viruses vs. marketing, bad code, misguided efforts
- progress and evolution
- organizing, sifting, haystack
- how to extend for organizational and enterprise use, vendors, alliances
- where to draw the line for this spec, clean it up, and distribute version 1.0
- analogous apps for Flickr, Friendster, LinkedIn — decentralized, distributed, P2P, etc.
- auto-Mensa group problem solving
- clubs, associations — prestige, exclusivity, revenue opportunities
- beyond structured query — cues
- distributed wikipedia, open directory — bypass web politics
- evolution of bits, bytes, files, folders, drives, systems, networks, users, communities — agent-oriented
- global data places, global database
- what is the opposite or complement of “base” as in “database”
- global demand pull — publish data to community (e.g., RSS) and “let it flow” — feed, caches, subscriptions
- prompt for knowledge
- seamless hybrid of human and agent effort
- clouds and emergent clouds
- being intentional
- identity tokens — subset for purpose
- difference detection
- transitive closure, composition, intellectual leverage
- managing secrets
- managing “The Big Picture”, top to bottom, inside out
- clear metaphor for agents as adjunct to the human mind
- issues related to the distinctions between bits and atoms
- the flip side of long-range vision: the immediate “feel”
- beyond IM/email: always communicating knowledge
- facilitation of coaching and mentoring
- personal information agents
- fiction: Impermanence Agent
- C2C ecommerce
- vetting: trust, reputation
- game theoretics
- Eliza: mock intelligence
- URI/anchoring: considered harmful
- reliably identifying communities
- name tokens, token servers
- server-free network computing
- reference vs. identity
- real world vs. digital identity
- evolving roles of money, value, pricing, cost, wealth, options, opportunity
- consumer federation
- consumer process flows
- game and decision theory — open games, dynamic equilibrium
- sensor networking
- managing the home
- context information
- effectively hiding complexity — agents to manage it
- free, cost, subsidy, deficit, reckoning
- opinion management, polls, surveys — value, compensation
- fanatics, zealots, and anarchists
- market-based mechanisms
- strong AI — not!
- self-organizing organizations
- autonomic networking
- emergence, emergent communities
- project workflow
- consumer collaboration on application development vs. “the developer” and “the development team”
- [natural] application aggregation
- consumer as a sovereign
- transparency: what’s happening and why
- tolerance for mistakes — lower the cost, make it easier to recover
- managing preferences
- focus on knowledge rather than specific apps — “knowledge-based applications”
- actor identification — who’s who and who’s in charge of what
- placement agents
- deep keywords — e.g., “job opportunities”, narrowing implicitly
- “Some people.”
- my agents are always on it even if I don’t know about it yet
- evolution of servers, virtual servers
- stuff finding us rather than us finding stuff
- diffuse network traffic — web servers considered harmful
- thinking (and typing) in knowledge
- the ultimate app: free-form entry of knowledge snippets
- triggers and workflow alerts
- nexus facilitation, based on information, not location
- facilitate working smarter rather than working harder
- information-based distributed computing rather than location-based computing
- economics: spectrum of subsidy from full payment for services to controlled, selective disclosure of consumer interests to vendors
- economics: software agents that read advertising streams and pass items of interest back to consumer
- facilitating the search for truth
- 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?
- autonomous agents vs. multi-agent systems
- disruptive technologies vs. disruptive consumers
- virtual bits
- blending software and services
- consumer media
- levels of knowledge management
- cooperative information agents
- Neopet as a next generation of the “Bob” concept
- where do agents run?
- where do agents store their knowledge
- email — where knowledge goes to die
- common sense knowledge
- domain-specific ontologies vs. upper-level ontologies
- coping with hackers
- agent systems, cooperating and collaborating agents
- elaborate on “mobile agent”
- nature of agent “thought processes”
- focus on complexity of “systems” — even a single consumer is a complex system
- systems: mutual interaction of parts
- the whole represents more than the sum of its parts and has a synergy of its own
- role of context
- emergence in systems
- understanding vs. knowledge
- truth vs. “a” reality and multiple realities
- accretion of knowledge vs. explicit collection
- collaborative applications
- agent-filtered advertising
- consumer benefits: productivity, enjoyment, more knowledgeable
- consumer electronic manufacturer interest
- how to be “in-touch” with the consumer market
- consumer-centric software agent technology
- distributed knowledge and infrastructure
- what do consumers consume and what do they produce?
- virtual organizations
- journeys of discovery
- web intelligence (WI)
- wisdom web and wisdom web agents
- knowledge ecosystem
- role of digital life
- active media technology (AMT)
- agent views of vendors as outside entities
- consumer protection via agents vs. government and lawyers
- the consumer “domain”
- 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.)
- Consumer Grid
- consumer choice — rational or not?
- equilibrium vs. disruption
- distinctions and similarities between challenges and opportunities
- organizing around individuals — democracy centered around consumers
- define: Who is a consumer?
- Roles and methods of linking
- knowledge vs. memories
- where do a consumer’s memories live?
- My Intelligent Agent(s) / Your Intelligent Agent(s)
- coping with hype
- images, illusions, surfaces, depth, distortion
- bridging the voids between types of media (text, image, audio, visual, data, time)
- coping with large volumes of media, even for narrow domains and niches
- collective action
- digital relationships
- creative commons
- the interface, intersections, conflicts, and collisions between culture and technology
- coping with the signal to noise ratio
- the nature of community
- what are you thinking?
- what are you feeling?
- what do you want?
- metadata, social and otherwise
- facilitate converation
- the nature of conversations
- the nature of participation
- coping with the relations between our past(s), present(s), and future(s)
- power of randomness — now for something completely different
- concept map/cloud/network/web
- facilitate storytelling
- Adopting knowledge, putting knowledge up for adoption
- knowledge seed for the knowledge tree of life
- consumer use of software agents for knowledge-based computing < 0.0001% of Kurzweil’s Singularity
- establishing and maintaining knowledge connections vs. device connections
- facilitate access to libraries, digital libraries, knowledge libraries
- facilitate grasping of knowledge, the complex, the simple and the seemingly simple
- questions as knowledge
- role of proof, certainty, and evidence
- coping with gossip
- try to find some books for reference
- managing hope and expectations
- coping with process vs. structure
- facilitating use of analogies
- just works, seamless
- identifying opportunities from both hot topics and hidden values
- content management vs. knowledge management
- some hidden category between knowledge and wisdom
- facilitate intuitive leaps and emotional intuition
- facilitate pattern recognition
- much more work on the economics, especially for non-visible agents
- the nature of knowledge
- extend your subconscious
- invisible servant
- remembrance agent
- app model: behind the scenes, automated
- talk to my knowledgebase
- scenarios and stories
- shared context
- focus on platform and knowledge infrastructure, not specific apps
- consumers own their data
- facilitate serendipity
- knowledge “publish and subscribe”
- facilitate enlightenment
- “aspiring” vs. “aspirations”?
- coping with priorities and sense of urgency
- consumer computing
- consumer-oriented knowledge-based software agent environment
- knowledge-centric versus site/location-centric
- role of music
- information retrieval (IR)
- facilitate “seizing the day”
- coping with procrastination
- coping with resolutions (e.g., New Years)
- facilitate a brighter today rather than the diffusion of a brighter tomorrow
- facilitate turning time into the timeless
- facilitate a journey into meaning
- gaining perspective
- facilitate discovery of possibilities
- paradigm of “tapping into” the knowledgesphere
- knowledge as a platform
- JIT knowledge
- knowledge as active data, not static
- flexibility vs. Mt. Rushmore syndrome
- coping with crises
- verbs vs. nouns — active processes vs. static results
- scenarios: options, risks, evolution of goals
- URI vs. UKI (context-independent, context-relative)
- the problems with web-site-centric URIs, alternatives
- parallel operation and asynchronous operation
- semantic markup
- velocity and acceleration
- organize by technology, specific app areas, variety of app “thrusts”, common app characteristics
- friends, neighbors, family, relatives, community interactions
- need for both formal names (specifications) and user-friendly nick names
- elaborate on identity issues
- agreements, computational agreements
- connecting knowledge, bridging gaps
- essences, core knowledge, meta-knowledge
- gatherings, meetings, online conferences
- grounding knowledge
- consumer reviews
- smart information (vs. knowledge)
- diagram: platform, apps, knowledge flow, storage, consumer, vendors
- knowledge mashup
- facilitate feedback
- facilitate monitoring credibility
- facilitate connecting dots
- what are current consumer “pain points”? — identity theft, privacy, ease of use, comfort, confusion, …
- the “real” Semantic Web
- facilitate strategic planning
- principles and and concepts for consumers
- facilitate unique sense of consumer identity
- facilitate picking and choosing and managing choices
- pathways, evolution, “knowledge paths”
- what do you/we really want?
- facilitate surrogate travel
- concept/term syntax vs. URI
- blog for this vision
- Semantic Web as an underlying storage/transport mechanism, but not the “true” knowledge layer
- knowledge as transparent ubiquity
- who is the intended audience for this vision? and potential customers
- quantum computing — the knowledge angle
- quantum information — quantum knowledge?
- entangled knowledge
- coherent knowledge
- knowledge complexity — modeling, measuring, reporting, managing, reducing, coping
- robust knowledge, fault-tolerant knowledge
- consumer white list
- e-commerce access to consumers
- develop use cases
- 2-page position paper (for conferences, workshops)
- facilitate persuasion
- facilitate creative writing, role-playing, plays
- agent system vs. MAS
- consumer rules (ala business rules)
- consumer products with embedded agent-based systems
- impact on society
- letting knowledge webs drive infrastructure software design — knowledge-based software design
- gadgets and devices, geeks and consumers
- Semantic Web and devices
- consumer-centric consumer devices
- vendor-independent/neutral computing
- consumer-controlled computing
- introduction for consumer-centric knowledge web
- coping with and exploiting implicit relationships
- consumer control
- consumer products with embedded agent-based systems
- consumer-centric knowledge vs. consumer-oriented knowledge vs. consumer knowledge
- consumer-centric knowledge-based applications
- what is the real “juice” that will drive consumer-centric knowledge
- consumer design (ala industrial design, but driven by and focused on the consumer) — consumer-centric design
- coping with emergencies
- intellectual capital — value
- knowledge as predictions
- non-text media knowledge
- auto-web site
- beyond answers, questions and knowledge
- facilitating engagement
- facilitating limited vendor access
- what is knowledge versus information?
- ontology vs. schema (ERD)
- relationship between knowledge and environment (and entities)
- digital lifestyle
- facilitate the connection, contrast, and separation of real-world and computational knowledge
- what is computational knowledge?
- you own your knowledge, your awareness
- facilitating structure and focus *and* facilitating free-flow
- facilitating journaling
- facilitate unraveling of mysteries and confusion
- “knowledge buddies”
- “knowledge tribes”
- facilitate finding your tribe(s)
- knowledge as adventure
- knowledge as narrative (story)
- “laws” of consumer-centric software agents
- consistently use consumer-centric software agent term
- knowledge capture: how, better models
- meaningful knowledge
- facilitating good ideas
- coping with “good” ideas
- an index or topic map
- facilitating paradigm shifts
- coping with paradigm shifts
- facilitating communities of practice (CoPs)
- facilitating communities of interest (CoIs), implicit as well as explicit
- interaction with and awareness of nature, the real world outside of human activity
- the agent paradigm
- portals, libraries, and directories
- common knowledge
- common concerns, common values — “auto” groups
- addressing pressing social problems
- facilitating reasoning
- generic application capabilities — factoring from specific applications and categories
- opportunities for distinctions between information and “content”
- mention and link to Agtivity.com, the concept web
- PDF version of this paper
- rationality and self-interest
- facilitate being concise
- agreements between individuals, within groups, and among groups
- maintaining attention
- joining ideas together
- gaining clarification
- response, thread of conversation
- knowledge: what’s the point?
- knowledge/idea/concept ecology
- intentional knowledge vs. incidental knowledge or accidental knowledge
- knowledge: linking and connecting
- who is “we”, “us”, “them”?
- ad-hoc, agile knowledge
- culture development
- transparency, opaqueness
- managing goals
- getting things done
- the magic of threes
- study, contemplation, thought
- knowledge silos
- requesting and giving direction
- leveraging — as a generic capability or measure
- comprehending consequences
- managing priorities
- participation — facilitating, coping
- filters, filtering
- sources, reliability
- abacus analogy for knowledge
- webs of inclusion
- institutional memory, knowledge
- knowledge planning — as if it were active and could be tracked
- outcome-oriented knowledge
- hybrid knowledge
- knowledge-driven processes
- knowledge wiring diagram
- strategic knowledge, knowledge of strategy
- feedback, guidance
- norm, norming — facilitate, coping
- what makes sense?
- service: essential, desirable, discretionary
- understanding costs of choices
- facilitate noodling
- budgeting as a specific application
- blissful ignorance
- parking lot(s) for issues
- facilitate community sustainability
- streamlining processes
- facilitate brainstorming
- facilitate vetting ideas
- constraints: identifying, managing, coping, utilizing
- email: content/knowledge, conversations/interactions, tracking/status
- hard truths
- sidebars in threads of discussion
- coping with folklore
- thinking in XML/knowledge fragments
- building knowledge chains, trees, webs
- role(s) of standards
- statistical analysis, pattern recognition for knowledge mining
- Beyond the Cluetrain Manifesto — consumer-centric, not merely consumer-oriented
- knowledge: structure, generation, dissemination, transferal, digestion, use, modification, reuse
- role of philosophy, personal philosophy, representing and using philosophies
- philosophical basis for knowledge: in general, and in particular
- identification and reference: identity, meaning, and reference
- linguistic theories
- social meaning
- rules: all aspects
- auto-rules — manually-constructed rules are too difficult to get right, to comprehend, to maintain, and to evolve
- knowledge versions
- knowledge constraint management
- knowledge appliance
- topic versus concept
- degree of conceptual precision
- loose knowledge versus tight knowledge: what is or isn’t known about tightness, ambiguity, context
- two-way knowledge expression: let a thousand parsers and text generators bloom
- link to Agent Technology Roadmap
- x versus management of x versus decisions about x
- theory of it all
- organizational memory (OM), distributed organizational memory (DOM)
- how close will the consumer knowledge web be to actually thinking?
- facilitating mutual respect
- Cyc “common sense” knowledge system
- pervasive knowledge
- adaptive knowledge
- ambient knowledge
- context-aware knowledge
- coping with subtlety
- facilitating philosophical inquiry
- annotation of knowledge
- facilitate investigation
- granularity of knowledge
- facilitate open knowledge
- interoperability of knowledge
- facilitate negotiation
- decentralized knowledge
- managing event calendars, personal calendars, and group calendars
- tenets of the CCKW
- social network analysis
- cooperative structures
- the knowledge economy
- knowledge workers
- coping with the elite, the high-priests
- facilitating systematic organization of knowledge and activities
- ethics and ethical knowledge
- how to make CCKW “viral”, promote adoption
- culture and cultural cognition
- express the consumer experience
- need a better acronym
- agencies: agents grouped by competencies — grouping consumer interests
- organizational memory
- surprise me — power of random behavior
- detail application categories
- detail app platform levels
- auto-forms — knowledge drives information gathering requirements
- beyond the bleeding edge, for now
- variants of the Consumer Web: presentation, information, knowledge, intelligent
- joint goal-seeking
- compiling and linking knowledge
- how to make the CKW appealing
- relationship between knowledge and intelligence
- knowledge, intelligence: what is the third leg of the stool? agents? consumers? the environment?
- linkage between knowledge and consumer activity
- list what consumers do or do not want to do or should do — norms
- facilitate escape, membership
- embracing knowledge
- skepticism vs. knowledge
- transparency of technology for the consumer knowledge web
- horizons: the view, pushing towards them, pushing past them, expanding them
- 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.
- managing demands for attention
- coordinating activities
- knowledge analogy to the classic PIM (Personal Information Manager): the Personal Knowledge Manager?
- knowledge analogy to Ajax, a group analogy as well
- no more “synchronize”
- deep context: more than simply superficial info
- Always On → Always Knowing
- knowledge portals
- obliteration: removal of mistaken or offensive knowledge — really?
- scope and scoping — as knowledge-generating activities
- importance: priority vs. significance
- impacts of potential actions
- need a consumer-oriented vocabulary, jargon, lingo, slang, dialects
- abstraction vs. specificity
- abstraction vs. ambiguity
- how to model abstractions — let software agents do it
- interactions between consumers and governments and other organizations
- process — as forms of knowledge
- forward updating of knowledge
- is knowledge ever really “consumed”?
- cost(s) associated with knowledge
- forbidden knowledge, classified knowledge
- probability, statistics, and uncertainty — statistical nature of knowledge
- the knowledge “agora” must feel comfortable and vibrant, not like a library, archive, or vault
- “knowledge vault”
- coping with fragmented intelligence
- “external” knowledge
- knowledge contained in images (or alleged) — “A picture is worth a thousand words” — challenge: how to access those thousand words
- pictures as symbols
- pictures vs. the language barrier
- the knowledge barrier
- panoramas, scenarios
- dirty knowledge, knowledge cleansing
- truth, lies, deceit, deception, fiction, wishes, hopes
- agents of ignorance vs. agents of truth
- completeness, accuracy, consistency, utility
- knowledge having a life of its own vs. “owned” knowledge
- knowledge communities
- building blocks for knowledge
- units of knowledge
- content vs. knowledge
- challenges, passions as forms of knowledge
- deployment of the CKW with today’s technology will result in ineffectual non-solutions and cause more problems than it solves
- InnoCentive — web-based community for scientific collaboration (“scientific challenges) — a model for some consumers
- objects vs. ideas vs. forces and tendencies
- trivia and factoids vs. substantial knowledge
- coping with kooks
- correlation vs. causality
- agents in knowledge (e.g., causes, causal agents)
- coping with difficult problems
- blank space vs. knowledge — knowing what we don’t know
- cooperative tasks
- knowledge processor, personal knowledge processor, group knowledge processor, domain knowledge processor
- the blank slate, tabula rasa — the power of, coping with
- Knowledge Machine, Mindstorms (Seymour Papert)
- how do we learn, how do we teach?
- economics of learning
- memory loss
- computer-assisted instruction (CAI)
- strength of memory
- idea processor
- powerful ideas
- intersection of consumer and academic knowledge
- how will consumers actually use the consumer-centric knowledge web?
- COW: Consumer Ontology Web
- Ontolite: a simplified methodology and language for expressing ontologies
- coping with propaganda and social control
- alignment of disparate knowledge communities: values, ontologies, views
- target vs. source, here vs. there, us vs. them
- explicit vs. implicit knowledge
- the nature of knowledge and intelligence
- what is an “answer”? how to manage “answers”
- registering interest
- better case for why agents vs. traditional or Web 2.0 approaches
- more on contrast with “The Singularity”
- parity between producers and consumers
- net on the “edge”, consumers in “middle”
- benefits to society
- channels, broadcasts
- conclusions (vs. facts)
- the initial data points: Web, Semantic Web, Cyc, Singularity, classic AI
- “total knowledge”
- downside of wider access to knowledge
- ontology alignment
- the classic MVC model, with knowledge as the fundamental model
- methods, methodologies, approaches, etc. as knowledge
- coping with dogma
- support for knowledge or an argument
- nature and role of reification in knowledge processing
- subsetting for kids, children
- representing novelty
- distilling knowledge: not just “nuggets”, but wisdom and opportunities
- coping with anarchy
- target the “next” generation: those who will be 8-years old in ten years — skip the current generation and their parents
- disruptive technology: disrupt current disruptions
- “consumer deep”: current apps way too superficial
- knowledgecast: what would it be like?
- knowledge as liberation
- distinguish economically-motivated knowledge
- distinguish goals from constraints (positive vs. negative)
- facilitate general planning
- structured consumer information as distinct from unstructured knowledge
- Cyc: too primitive, low level, and fragile for consumer use
- “That’s not what I meant”
- do what I mean (DWIM)
- visualizing concepts
- diffusion, tangentials
- knowledge variables, functions
- mnemonics, memory aids
- dealing with criteria for best/better
- tacit knowledge, how to access it
- how do consumers think about knowledge
- “that’s just semantics”, bad semantics, insensitivity to semantics
- facilitating focus
- sources of knowledge
- ripple effects
- “connecting” with knowledge
- analog to a synapse
- mind vs. brain
- knowledge roadmaps, designs, schemes, outlines
- knowledge catalog
- objective vs. subjective knowledge
- FAQs as knowledge
- speaking each other’s language
- the MIT Logo experience, kids, early learning, teaching
- beyond the Cluetrain Manifesto — consumer-centric, vendors do the compromising to gain access to consumers
- KT: Knowledge Technology (ala IT)
- major challenges: ontology alignment
- C-KOW: Consumer Knowledge Ontology Web
- death of email: email -> “create knowledge”
- knowledge definitions: collect them
- can you possess and make sense out of knowledge without ontology?
- ontological commitment
- unit/atom of knowledge — what is the least you can know?
- does knowledge want to be free? what does that mean, entail?
- remix/mashup knowledge
- assertions vs. facts
- power mining, automatic ontology mining
- knowledge claims
- creative destruction applied to knowledge
- economic signals
- activity-oriented knowledge (e.g., hobbies, games, sports, clubs)
- context for focusing agents
- knowledge as a form of currency
- nature of a piece of knowledge
- correlating fragments of knowledge
- knowledge as threads or strands, strand length — strands of knowledge
- analogy of nerves and neurons: axons and dendrites
- fingers/arms of knowledge
- facets, purposes
- using knowledge without “knowing” it
- relevance is relative — Theory of Relativity for knowledge
- navigation through knowledge/ignorance
- libraries of ignorance
- capturing judgment
- role of traditional libraries — acquiring and organizing books, knowing what’s where
- grapevine, gossip, water-cooler “knowledge”
- knowledge grazing
- subjects matter experts
- eLearning — knowledge about your level of knowledge
- virtual knowledge
- knowledge templates
- intuition — what does it really mean — meaning without knowing
- deep study
- discussion re: knowledge acquisition, dissemination
- variation from traditional “field of computation”
- what is the nature of “constructing” knowledge?
- semantic wiki, knowledge wiki
- personal knowledge management system
- non-technical users — what does that really mean?
- role of structured text
- “brewing” knowledge — tea metaphor
- social network intelligence
- agent intelligence
- software agents that can “work” with knowledge without comprehending its full meaning — knowledge as the more appropriate “unit” for software agent activity
- knowledge topology
- ignorance as the unit/seed of knowledge — ignorance + desire + motivation + aptitude + skill + persistence
- role of motivation in knowledge, role of knowledge in motivation
- web farming
- the nature of the role of a “web” in knowledge and software agent activity
- knowledge foraging, rummaging
- distributed problem solving
- macro knowledge vs. micro knowledge
- role of clustering, both that which is inherent in the structure of knowledge, and when querying
- the significance of The XML Revolution
- channel concept — pro vs. con
- model description
- knowledge is ubiquitous
- dictionary as starting point for knowledge, plus specialized glossaries
- grasping, comprehending
- being stuck or hung up on “bad” knowledge
- liana as threads or vines, “knowledge vines”
- jungle metaphor, The Knowledge Jungle
- information flow, knowledge flow
- concept vs. keyword
- unit of concept
- concept formation process
- semi-knowledge, semi-ignorance
- learning processes
- knowledge-deficient environment
- knowledge environment
- what does it mean to introduce a knowledge infrastructure to consumers
- nature of introduction of concepts
- knowledge as overlapping “clouds”
- knowledge as planet, a solar system, a galaxy
- brain and nerves as a knowledge metaphor, nature of “computing” knowledge
- knowledge beast — it’s alive
- jokes, poetry, literature, prose as knowledge
- conventional wisdom
- digital revolution, the knowledge revolution
- knowledge colonialism
- knowledge sector
- text -> information -> knowledge
- mobile knowledge, position/context/locale/environment-dependent knowledge
- knowledge is power, power is knowledge
- questioning knowledge
- challenging basic assumptions
- call-center management, knowledgebase
- relating concepts
- dangerous ideas
- nature of analyzing knowledge, knowledge analytics
- knowledge warehouse
- PowerPoint as a knowledge metaphor, bullet points as a knowledge metaphor
- knowledge modules
- nature of learning, nature of teaching
- knowledge vs. content
- 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
- briefings as knowledge
- knowledge vs. experience
- QA/QC for knowledge
- is it fiction to keep consumer and vendor knowledge separate?
- reputation: establishing, validating
- video knowledge
- waves, vectors
- impact of the laws of physics on knowledge (meta-knowledge)
- harvesting knowledge
- “wisdom of crowds”
- knowledge-sharing tools
- knowledge outreach vs. search
- knowledge web as a marketplace with non-monetary, but functional economics
- what is the invisible hand of knowledge
- ideology and knowledge: impact, constraint, influence
- illusions: imperfect knowledge
- the dark side: dysfunctional knowledge or knowledge used to enable dysfunctional behavior
- how to avoid or copy with knowledge “running out of control”
- knowledge about human nature — is it special?
- feeling that we can make a difference — existing knowledge not a monolith
- coping with and resisting commercial pressures and influences
- coping with and resisting efforts to “control” knowledge
- safeguarding knowledge
- fear of knowledge: understanding, coping, adapting
- knowledge of behavior: what do we really know?
- relation of knowledge to human consciousness
- upheavals of thought: coping and even facilitating
- encoding the history of ideas
- the cosmos: nature, structure, and evolution
- drives and motives
- the nature of adaptation of knowledge
- group-limited knowledge (scope)
- prevailing knowledge: coping with and acknowledging
- “rooms” and “houses” of knowledge: boundaries and barriers
- knowledge landscape, terrain
- heterogeneous nature of knowledge
- list of people and place names as part of core
- world almanac as part of core
- thesaurus as part of core
- core lists need to be structured, eventually, but provide an initial base of initial concept “points” (ala URIs)
- “knowledge” contained in popular non-fiction books
- “knowledge” contained in works of fiction
- cultural artifacts as knowledge
- archiving of knowledge — knowledge snapshots
- nature of noise in knowledge
- what does it mean for knowledge to be “digital”?
- analog as a superior model for knowledge compared to digital
- approximating analog knowledge using digital techniques — pursue ever-finer granularity
- personalized knowledge: MyKnowledge, OurKnowledge
- role of psychology in knowledge
- extrapolation, interpolation
- inference: what the knowledge web does vs. what the consumer does
- dangerous inferences
- knowledge vs. policies, social policy
- memes, anti-memes
- unspeakable ideas
- experiments: role in generating, testing, and building knowledge
- rational autonomy
- modes and differences in rates of knowledge uptake
- personal science, worldview
- walled gardens
- intermediation: coping and facilitating
- explanations vs. knowledge
- power of examples, limitations of examples
- knowledge engines
- approaches to accelerating knowledge uptake
- focusing on categories
- news as knowledge
- chatter, ramblings, rants as knowledge
- levels of description
- personal theories, consumer theories
- guiding forces
- knowledge in complex adaptive systems (CAS)
- dark knowledge: it’s there, but we don’t sense it
- mechanisms of association of concepts, symbols, and meaning
- meaning vs. value
- conformity vs. dissent
- blueprints, “genes”
- knowledge of computation
- knowledge in the context of virtual reality — virtual knowledge?
- coping with cunning — attempts at excessive control
- facilitating free-will
- a day/week/month/year/life in the life of a consumer
- as the Web grows, signals get weaker and noise gets stronger
- the unknown — acknowledging and coping with it
- human needs
- wonder — acknowledging and facilitating
- the edge, the frontier — the difficulties
- not knowing — an expectant knowledge
- accommodation of disparate knowledge
- limits of awareness
- managing lists, fascination with them
- what are the inherent and potential limits of a consumer-centric knowledge web (e.g., expressive power)
- knowledge-exchange file formats, embedding knowledge in a PDF
- knowledge as an object vs. knowledge as a process — integrating the two
- nuisances from the perspective of knowledge
- what is the “perspective of knowledge”?
- making sense of stories — what is the knowledge contained in a story?
- recognizing and representing synergies
- what is relevance?
- measuring utility of knowledge
- boundary between mental and non-mental
- knowledge of abilities vs. skills
- influence of knowledge on the human brain itself
- plasticity of the human brain
- wending: facilitating slow, tedious processes
- the human spirit
- facilitating connections between people and group as much as connections between disparate knowledge
- hard problems
- knowledge of human relationships, human nature
- what “what that might mean” means
- coping with change, the speed of change
- anticipation: acknowledging, managing, coping, facilitating, exploiting
- role of education in knowledge
- variable interpretations of knowledge
- spheres of knowledge, agents that move between them
- information impinging on our senses
- judging and measuring meaningfulness
- implicit knowledge, learning
- non-verbal knowledge
- career advancement
- declarative knowledge, non-declarative knowledge
- information environments, knowledge environments
- what does knowledge accomplish?
- knowledge choices: accept, reject, defer, tolerate, influence
- faith-based knowledge
- knowledge upheavals
- literacy and knowledge
- knowledge divides
- competency: acknowledging, coping, facilitating, managing
- great sources of knowledge
- gatekeepers: pros vs. cons
- demonstration vs. speculation
- degree of rigor, lack of rigor
- basic principles
- extra-terrestrial knowledge — yeah, right, but still…
- fitness functions for knowledge, faked-fitness
- “what if” — is it really a form of knowledge, or is it simply a dysfunctional form of knowledge
- nonlinearity of the human psyche
- the idea of ideas
- facilitating quests
- manifest knowledge — what is it really?
- fundamentals — if they exist at all
- central truths — if they exist at all
- fundamentally, what is a topic?
- knowledge contained in slogans, mottos, and one-liners
- what is the nature of novelty?
- shared understanding
- intergenerational alignment
- forgotten gems of knowledge
- conservation of mind-space — too much knowledge, too little space in our personal minds
- coping with cues
- understanding mechanisms — “why” — knowing why
- pre-linguistic concepts
- nature of emergence in all knowledge
- networks of knowledge: what is the nature of a network?
- role of hierarchy in knowledge
- role of surprise in knowledge acquisition and adoption
- role of authenticity — when is it no so important?
- role of insight, insight itself as knowledge, observation vs. insight
- the sport/game of knowledge
- justification for conjectures, beliefs, and theories
- when/can a knowledge web become aware of itself?
- implicit coordination of knowledge — coordinating knowledge without explicitly coordinating it
- predation and parental supervision issues for minors
- Danny Hillis’ Aristotle (The Knowledge Web) on John Brockman’s Edge
- 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.
- 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?
- value and dis-value of “guided” learning/knowledge
- hunter-gatherer approaches to knowledge vs. farming vs. alternatives
- huge numbers: coping and facilitating with them
- “know what I know”
- the brain is a machine … what is a machine?
- to what extent is some knowledge instinctive or innate?
- does it make more sense to speak of being consumer-relative rather than consumer-centric?
- a homunculus as a model of human/human-like capability and perception — pros and cons
- CCKW synergism: empowers software agents and requires software agents as well
- knowledge is the “juice” needed to get software agent technology really moving
- facilitating the adoption (and adaptation) of new habits, coping with existing habits
- issues related to instilling new habits — pros and cons
- what is the nature of “instill” and how do we deal with it?
- issues related to motivating and avoidance of de-motivation
- Seymour Papert’s thoughts on (against) the concept of a super tutor or even an artificial tutor
- “knowing skills” (ala learning skills) — what does it take to “know”
- indirect attacks on The Knowledge Problem
- assessing “domain competence”
- parables as carriers of ideas [this is a very important idea]
- illiteracy: challenges and opportunities
- 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
- will the knowledge web actually make us all smarter, or simply allow us to be dumber?
- upper ontologies and common ontologies and core ontologies to facilitate domain-specific ontologies, group ontologies, and personal ontologies
- situation ontology
- joy of learning, discovery vs. mere utility
- CCKW not smart enough to tutor consumers
- civic knowledge to facilitate citizenship
- what is analog knowledge?
- sustainability of a knowledge ecosystem
- filters as knowledge, tools as knowledge
- work “outside the box”
- coping with arrogance, arrogant knowledge
- coping with attitude
- consumer energy usage and attitudes as a potential app
- “the check is in the mail” — what does it (anything) mean vs. what does it (anything) *really* mean?
- pervasiveness of random processes — what does it mean to have knowledge about a random process?
- role of narrative and stories in knowledge
- limits on knowledge in general
- how might a general knowledge web be distinct from a consumer knowledge web
- “on its face” — what does this really mean, in a deep sense for knowledge?
- the inherent connection of knowledge to experience
- relationship or distinction between thought and knowledge
- explaining yourself
- proposals as a specialized form of knowledge
- animating meaning — turning knowledge into stories for evaluation and explanation
- the nature of abstract thought
- placebos: what’s the analogy for knowledge, if any? — knowledge as a “treatment”
- hormones: what’s the analogy for knowledge, if any? — making knowledge more appealing or effective
- relationships between levels or degrees of cognition and knowledge
- knowledge “mirrors” and mirroring and mirroring mechanisms
- is a knowledge web really a “disembodied mind” or is it something rather different?
- coping with difficult ideas — ascertaining true and significance, if even possible
- coping with fences, borders, abysses, and other obstacles to the pursuit of knowledge
- knowledge as a goal to pursue vs. knowledge as a continuing inflow of “ideas”
- subtle properties
- contrasting the “what” and “who” and “where” and “when” with the “how” and “why” and “what does it mean?”
- focus on problems and questions and curiosity and need
- authority for knowledge (as in authority of a source)
- the nature of migration of knowledge
- significance of age vs. youth in openness to knowledge
- role of skepticism — pros and cons
- what are the actual building blocks for a consumer-centric knowledge web, the foundation, the structure, and the infrastructure as well?
- is there really “too much knowledge”?
- specialization: coping and facilitating
- bridging between disciplines
- repackaging highly technical knowledge for popular consumption
- Vannevar Bush’s Memex
- what is “good enough” as a primitive, but useful approximation of the ultimate CCKW?
- how is a knowledgebase different from an information database, besides being distributed?
- coping with out-of-date knowledge
- knowledge of common problems
- human reinforcement of knowledge, machine-deduced or otherwise
- machine reinforcement of tentative human beliefs
- need and roles for human authors and knowledge engineers vs. automated knowledge engineers
- CCKW is a base for tutoring software, but not a tutor itself
- role of timing in knowledge
- gaining mastery of a subset of knowledge
- starting points for learning
- medium for publishing knowledge
- annotation: is it a distinct activity, or should it be integrated with the knowledge capture process itself?
- role of encyclopedias in the CCKW
- role of professional/refereed journals in the CCKW
- mapping and cataloging the CCKW
- human desire to share knowledge
- seals of approval, vetting (a form of authority) — is authority distinct?
- peer review — what does this mean in the context of consumers?
- potential for too much of the blind leading the blind?
- one person’s wheat is another person’s chaff
- power law effects in knowledge (small amount of highly-referenced knowledge, large amount of little-referenced knowledge) — not to be confused with authority
- is knowledge inherently free? is it ever really free?
- knowledge guides
- focus attention on early adoption by kids (ages 8–15) — more flexible and open to novelty
- how to broaden the spectrum of knowledge authoring capabilities
- initially build a knowledge web broadly, and then backfill niches, as opposed to focusing on narrow niches first
- role and nature of critical mass for a knowledge web — is it really an issue or not?
- underlying principles (for concepts and topics) — in relation to how we structure knowledge
- coping with hand-waving
- facilitating the opening of minds
- role of convergence in knowledge
- role and nature of paradigms in knowledge, both paradigms as knowledge and the process of paradigms
- role and nature of parody in knowledge
- is the simple stuff really simple, or is that an illusion and getting the simple stuff really, really right is the key?
- is knowledge really exploding, or are our metrics and tools just really bad?
- communities based on specific scenarios, common experiences, and specific needs
- knowledge of common experiences, or even uncommon experiences
- James Burke’s Knowledge Web (Connections) — how can some of this knowledge be accessed and manipulated?
- social patterns as forms of knowledge
- emergent knowledge — how to treat it in contrast with established knowledge or accepted knowledge
- implicit semantics
- factoring in cost of errors when considering relevant knowledge
- cultural knowledge
- folk ontologies — make it a real concept — how informal communities develop ontologies
- trends and fashions and fads — transient knowledge
- semiotics — syntax + semantics + pragmatics (Eco, Peirce)
- meaning to the individual vs. meaning to the group or community
- cultural semantics
- ambient semantics
- rhythms as forms of knowledge
- relationships and distinctions between interests and intentions
- knowledge processes as a richer form of knowledge
- summaries and summarization as forms of knowledge and knowledge processes
- coping with the obnoxious
- attention as a form of knowledge
- RFID to gather knowledge from the consumer’s environment, allow the consumer to optionally and selectively “leave” a knowledge trail
- make media (audio, images, video) object-based so that it can be knowledge-rich
- interacting with intelligent objects
- working with snippets of knowledge — what are they and how do they convey and signify knowledge
- motivation: seeking truth vs. utility
- addressing and moderating tedium
- how to mesh curriculums, courses, lessons, tasks, and goals with raw knowledge, if that makes sense at all
- expertise as a distinct form of knowledge
- craft as a distinct form of knowledge
- CCKW is *not* a teaching system or substitute for teaching — a lower-level tool for accessing raw knowledge
- what is raw knowledge? bounds, limits, distinctions
- H. G. Wells’ World Brain (1938)
- relationships and distinctions between knowledge and ideas
- coping with misconceptions and misunderstandings
- coping with resistance to novelty or established practice or eclectic thinking or isolated thinking
- the nature of being intrinsic
- role of interpretation vs. clear facts
- the raw power of the single mind vs. collective power — respective roles, relative value
- role of simplicity contrasted with the utility of complexity
- role of appreciation in accessing knowledge
- knowledge as more of a process than an object
- the need for Computer Science 2.0 to provide the conceptual basis for approaching knowledge webs
- work on 5-page popular article on the CCKW, contrasting with current Web and Semantic Web, and other KW proposals
- role of unconscious mental processes in knowledge processing — role of unawareness or unconscious awareness
- the disconnect between how humans normally interact and how computer systems present information and interact with humans
- emotional states and expressions as knowledge
- psychological issues related to knowledge processing
- coping with cruelty and bullying in knowledge
- the quest for fundamental explanations
- unconventional experiences
- knowledge competition
- epistemology: the nature and grounds of knowledge, the study of the nature and foundations of knowledge
- issues of intellectual power
- cooperative venture: consumers and their agents and the agents of other consumers and those other consumers
- knowledge related to consumer commercial transactions
- predictive knowledge
- knowledge about causation, cause and effect, contrast with coincidence and correlation
- the nature of inference, how to segue to other techniques
- computational approaches that make real sense for knowledge processing
- what is a knowledge processor? (ala word processor?)
- connecting to your peers: using knowledge to locate your peers
- knowledge about “my projects”: sharing and finding potential collaborators
- what does it really mean to be “well-informed”?
- introspective knowledge, conscious awareness
- sensing when you are “in the dark”
- barriers to knowledge
- frontiers of knowledge
- what is the analogy for knowledge to the physics concept of a “field”? — “patterns of order”
- model of knowledge processing that reflects both Darwin (evolution) and Einstein (relativity) — what else?
- focus on knowledge as a process (knowledge processing) rather than as an object — metaphor of watching a movie
- CCKW is really a “knowledge processing environment”
- regularities: are they really rules or potentially transient forms of knowledge?
- unifying the view of knowledge with communication and signification
- role of myths in knowledge
- role of rhetoric in knowledge, combined roles of rhetoric and ideology and mythology
- are narratives and stories the same concepts or are they only partially related and somewhat distinct?
- forms of knowledge that are symbols far beyond the raw knowledge itself (e.g., well-known books and treatises)
- how to incorporate books into the CCKW, especially wrt IP issues (copyright)
- morality as yet another form of knowledge, coping with the morality vs. truth issue
- harsh reality: there is no “truth” per se in a global CCKW
- need to accept and cope with “the enemies of science”
- criticism as a form of knowledge
- refutation as a form of knowledge
- aesthetics as a distinct form of knowledge
- coping with apparent “formula” that mask complexity or inconsistencies
- defining a position or interest based on questions rather than simply asserting facts — may be a lot easier and much more honest
- is their an “invisible hand” that guides the “market” of knowledge processing?
- scams and hoaxes and frauds and cons as forms of knowledge
- coping with “folk wisdom”
- are the distinctions between qualitative knowledge and quantitative knowledge as clear as they are made out to be?
- characterizing “defining differences”
- characterizing “to what degree”
- coping with blurring of boundaries
- characterizing (alleged) continuums and smooth distributions
- drawing lines: issues, coping, facilitating, managing
- supporting both specialization and generalization, but “specialized” support for both
- differences as a distinct form of knowledge
- need a small set of knowledge processing operators to master all of this complexity
- organizing principles: for knowledge overall and as a form of knowledge
- entanglement of knowledge
- what does anyone really know? — what we think we know vs. what we actually know
- role of cognitive dissonance in knowledge
- specific knowledge processing capabilities focused on facilitating cooperation
- role of “a theory of everything”
- concept of “relevantly similar” when matching concepts — observer-specific concepts
- representing and processing visual knowledge
- mind-independent truth — pros and cons
- significance of clever explanations
- role of common threads in knowledge processing
- framing and re-framing knowledge, meta-framing
- coping with manipulation and even brainwashing
- facilitate counterintuitive thought
- coping with unintended consequences
- role of chaos theory in knowledge processing
- is too much knowledge an inherently bad idea? is there really a concept of knowledge overload?
- dynamic ontologies are essential — the world is constantly changing, emergence is a real factor
- economies: a form of community with rules, authorities, methods, opportunities, and specialized knowledge
- smart things and ubiquitous smart things — a whole new level of forms of knowledge and knowledge processing
- knowledge and knowledge processing related to the boundary between the real and cyber worlds
- acknowledging and coping with “the limits of science”
- coping with “knowledge” that is neither measurable nor predictable
- codifying “what matters”
- coping with “fashionable certainties”
- knowledge as a “force”
- spiritual knowledge — unique requirements?
- collective consciousness: distinct forms of knowledge
- shards of knowledge
- “the tree of knowledge”: what is it, really?
- audience-friendly knowledge: pros and cons
- emotional intelligence
- time: what do we really know about it?
- strict distinctions: how strict are they and how distinct are they?
- the distinction between reality and fiction: objective and subjective difficulties
- outrage: aspects that are a form of knowledge
- defense of ideas as a distinct form of knowledge
- is it really “time” to pursue the consumer-centric knowledge web, or is even the research still beyond our intermediate-term grasp?
- coping with distortion and exploitation
- welcome and unwelcome knowledge
- grand claims and wild promises
- value-free knowledge: pros and cons
- role of passion in knowledge and knowledge processing
- role of articulation in knowledge processing
- role of “unlearning” in pursuit of knowledge
- “semantic annotation” is *not* a user-friendly approach to producing a knowledge web
- role of multiple “angles of attack” on knowledge, points of view
- important knowledge: what are the most important things to know? top 100? top 10? top?
- what are the most important things that the CCKW can do for any consumer?
- what fears can the CCKW help to curb?
- heuristics as a distinct form of knowledge
- fuzzy knowledge — a distant cousin of fuzzy logic
- spam as a form of knowledge (e.g., knowledge of scams)
- assessing the value of knowledge — down to slight, zero, and even negative
- distinguishing the cost and value of knowledge
- dialogues as a component of knowledge
- networks of relationships as a distinct form of knowledge
- seeking knowledge (apart from a specific need) as a distinct knowledge processing activity
- the nature of expanding a knowledge web
- what is the nature of the distinction between that which is knowledge and that which is not knowledge?
- non-knowledge: a dead-end, terminal piece of information which cannot be further interpreted as knowledge
- artificial distinctions as a distinct form of knowledge
- continuity of knowledge (e.g., people retire and take their knowledge with them)
- “Email is where knowledge goes to die” — great source for knowledge mining
- meetings: big challenge representing the “group” knowledge expressed in a typical meeting
- the world of statements: how is knowledge expressed?
- the origin of knowledge: what was the first knowledge?
- the nature of compounding of knowledge
- OWL — how much of it can be reused or used as a foundation for a richer and more robust knowledge platform?
- an “assembly language” for knowledge vs. a user-oriented higher-level language — need both, and of course the runtime support
- an interpreter for the knowledge platform vs. an optimized compiler for specialized, high-performance apps — need both
- distinguish casual knowledge from power knowledge or automated language — many forms of language, not one size fits all
- need a new term for “machine-processable knowledge” — automated knowledge? machine-analyzed knowledge?
- state of the art: keyword text and tags (metadata)
- “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
- what applications will be *possible* with a knowledge web that would *not* be economically feasible with the Web or even the Semantic Web?
- true reuse of knowledge: use by others for applications not foreseen by the creator of the knowledge
- concept of an “open world”: knowledge is never “complete”
- law: interesting challenge since lawyers and courts have a highly-technical view of law, but consumers need access to some amount of that knowledge
- automatically connecting isolated pockets of knowledge
- reconciling differences in terminology — both syntactic and semantic differences
- mappings are distinct forms of knowledge
- “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.”
- role of mathematical rigor and formal analysis in knowledge processing: pros and cons — level of research needed
- browsing knowledge: need innovative approaches — more research — partially collapsible 3-D “outline”
- analogy to an outline processor for knowledge webs — with sophisticated collapsing capabilities
- “pocket guides” for any area of knowledge as a starting point for searching
- 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
- example: capturing “Intelligent Design” as knowledge, regardless of whether you “believe” in it
- facts: knowledge is much more than a collection of facts
- let a thousand user interfaces bloom — no idea which types of UI will really give us quantum leaps forward
- nature of situated knowledge
- key challenge: consumers tend to be quite fickle and finicky — the CCKW should *exploit* those characteristics
- support mechanisms for active listening — reinforce knowledge for both the writer and the reader
- distinction between a “concept web” and a knowledge web — knowledge is more than simply a network of concepts
- 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”
- various interpretations of the term “knowledge web” — even informal, loose-text, and old-fashioned hyper-linked Web pages
- lessons learned from Wikipedia
- relation of standard topic maps to an overall knowledge model
- Project Halo — Digital Aristotle — Paul Allen, Vulcan Inc.
- utility of using geometry for representing complex knowledge
- making knowledge more accessible: goal: making knowledge immediately/instantly accessible
- exploit the synergy between webs of knowledge and webs of people
- uncovering hidden linkages or connections or relations
- meaning of “core knowledge” — variety of cores
- nature of “media” and its interaction with knowledge and knowledge processing
- “knowledge links” or “semantic links” or “semantically meaningful links” vs. hyperlinks — labels, attributes, concept/meaning etc.
- knowledge acquisition processes as a form of knowledge
- description of processes vs. “facts”
- distinctions between structure and content
- federated knowledge
- overlay networks for the knowledge web to enable selectivity
- using the knowledge web to facilitate cross-fertilization and building bridges between communities
- a knowledge discovery scripting language
- knowledge web as a digital brain — no a great analogy, but may have value for popularization
- role of history in knowledge processing
- support a variety of discovery modes, ranging from rapid scan to fine-tooth detail
- acknowledge that the best we can ever do will always be only an approximation of true knowledge
- case: why has the Open Directory Project (DMOZ) failed so miserably when it had such great promise
- themes as a distinct form of knowledge
- nature of knowledge life cycles
- logging knowledge
- role of thriving in chaos to knowledge processing
- role of biography in knowledge transfer
- the distinctions between “what we know”, what is in our minds, and how we represent in the real world “what we know”
- role of conflation — pros and cons
- knowledge structures of the human mind vs. computational knowledge structures
- mental knowledge: knowledge as it exists in human minds
- media knowledge: knowledge as it exists in physical media, including speech
- computational knowledge: knowledge as it exists within computers and is transmitted across computer networks, including decoding of meaning
- knowledge vs. reality vs. perception
- role of understanding in knowledge and knowledge processing
- “how to” as a distinct form of knowledge
- the spread of ideas as a phenomenon to be studied and promoted
- knowledge as an ecology (knowledge ecology) rather than simply a process or artifacts — actually, many overlapping knowledge ecologies
- body of knowledge: what defines the boundaries
- consumer information: what vendors and marketers crave
- visible knowledge, knowledge visualization
- zero knowledge: ala privacy and security
- role of education resources to consumer knowledge
- methods of coping with missing or fragmentary data
- indigenous knowledge or local knowledge: unique to a given culture or society
- role of ego in knowledge: reality and coping and avoiding
- machine-decodable knowledge: complements “encoding” of knowledge
- Knowledge Management does not have a beginning and an end. It is ongoing, organic, and ever-evolving.
- a consumer’s personal knowledge assets or knowledge wealth
- uncovering hidden knowledge
- communities of gossip: acknowledging their existence and coping with them
- MySpace and Facebook: lessons learned
- short article: identity management, privacy, and identity-shielded content
- best practices for knowledge processing
- knowledge in the small vs. knowledge in the large
- personal knowledge management (PKM)
- community memory
- example app: nutrition information
- example app: folk medicine: pros and cons
- resources as a distinct form of knowledge
- federated knowledge, knowledge federations, federated knowledge web
- “herding” as a metaphor for knowledge processing
- KAT — Knowledge and Agent Technology
- consumer-oriented: the consumer is in the loop
- consumer-centric: the consumer controls the loop
- example app: “products I’d like to buy”
- knowledge fragments as a preferred “unit” for expressing knowledge
- knowledge as a snapshot: emphasize the open-ended, dynamic nature of knowledge
- what is the knowledge analogy to land — the ultimate foundation
- the nature of holistic views of knowledge
- role of taxonomies in facilitating the sharing of knowledge
- nature of knowledge repositories
- how are we all connected? the many mechanisms
- dictionary stacks: common terms as foundation blocks, layers of ever-finer specialization and personalization
- core truths
- knowledge ecosystem mapping
- personal inquiry
- decision support systems (DSS) — from a consumer perspective
- knowledge inventory
- knowledge-intensive activities
- knowledge continuity management
- the complementary nature of the intuitive and the rational
- knowledge divides
- knowledge transformation
- social dimensions of knowledge
- knowledge leaders, knowledge leadership
- vital knowledge
- central question: to what extent can we exploit the field of knowledge management as a basis for forming the consumer-centric knowledge web?
- examples and counter-examples as distinct forms of knowledge
- utility of knowledge programming (LP)
- approaches to modeling as distinct forms of knowledge
- communication of hypotheses
- beyond DNS: new ways to communicate location on the Internet in the context of the CCKW
- beyond vendor-controlled servers: where and how is information stored in a way that is controlled by consumers
- define consumer: not simply producer vs. consumer, but “the people”
- federated ontologies: stitching and compensating for misaligned concepts, even for seemingly simple concepts
- 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
- a research narrative as a distinct form of knowledge
- system of thought as an aggregate “unit” of knowledge — what does it really mean?
- layers of knowledge — what does it really mean to “layer” knowledge?
- knowledge-centric view of organizations and systems — is this reasonable, or is it a myth and systems need to shift towards being consumer-centric?
- knowledge integration: this is the big problem, especially with the Semantic Web, but also the big opportunity
- ontological alignment: this is the main technical problem behind knowledge integration
- 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]
- organizations that know how to do things [Winter 1993]
- knowledge: what’s left after you remove all of the raw data and structured information
- distinctions and synergies between narratives and conversations
- from a practical perspective, how can consumers validate knowledge?
- distinctions and synergies between the worlds of reality and theory
- rhetorical convenience: balancing between coping with it and facilitating it
- role of salience in knowledge
- role of learning-strategies and learning strategies, both in capturing knowledge and discovering knowledge
- perspectives of psychology for capture, storage, dissemination, and discovery of knowledge
- perspectives of economics for capture, storage, dissemination, and discovery of knowledge
- the nature and role of mastery of knowledge
- role of “social facts” in knowledge [Durkheim, 1982]
- distinctions and relationships between “know how” and “know what”
- what does it mean for knowledge to be “digitized”? Is there a better way, an alternative to “digitizing”?
- data feeds as distinct forms of knowledge — e.g., relationship between the feed contents and the actives processes behind the feed
- knowledge processing: initial perception, mental processing, recording, communicating, perceiving (the recording), assimilating
- role of memes in knowledge processing
- what are the different kinds of knowing?
- roles of will and motivation in knowledge processing
- obstacles to broad-scale knowledge processing imposed by tacit knowledge
- forms of selectivity as distinct forms of knowledge
- the knowledge flood, the knowledge ocean
- natural cognitive processes, natural knowledge processing
- human factors at the knowledge level, as opposed to at the user-interface level
- programming software agents with the knowledge of how to figure out how to learn, as opposed to pre-programming them with specific learning faculties
- identity: successor to name and email address, including partial and selective anonymity
- software agent technology as an “API” for consumers and their knowledge and services
- where will a consumer’s software agents “be”? Answer: everywhere, but nowhere in particular.
- what is the nature of a preference?
- implications of/for CCKW for/of the very young, the very old, the very disabled, and the very different
- coping with the wide varieties of knowledge comprising a plan or strategy or even an approach — e.g., facts vs. expectations and hypotheticals
- relationship between human capital and knowledge, value of promoting human capital in pursuit of getting greater value from knowledge
- enhancing individual effectiveness
- adding “game mechanics” to knowledge processing to make it more appealing
- the nature of cliques: what are the incentives and disincentives, impacts, coping
- roles of core groups in knowledge processing, both creation and assimilation
- plumbing: the aspects of the CCKW infrastructure that are strictly distinct from any knowledge content
- knowledge of norms and normative knowledge
- digital lifestyle aggregator (DLA) — seems overly complex and cumbersome and unlikely without a true knowledge infrastructure
- Lifestreams as a metaphor and model for organizing much of a consumer’s knowledge [Gelernter]
- conversion between tacit and explicit knowledge (T-E, E-T, E-E, T-T) [Marwick, 2001]
- the tacit dimension of knowledge [Polanyi, 1996/1997]
- data vs. speculation — bases for knowledge claims
- cognitive frameworks
- the nature of expertise vs. tacit knowledge
- problem-solving and troubleshooting as distinct forms of knowledge
- specification of problems and scenarios as distinct forms of knowledge
- global virtual bit storage network as a key infrastructure component, plus the robust network to access the bits
- metrics for assessing the health of a virtual bit (e.g., degree of redundant storage, multiple access paths, access time, degree of privacy, etc.)
- distinguish privacy of content vs. privacy of ownership vs. privacy of identity
- add a slide to the PPT presentation on the nature of knowledge: mental, tacit, explicit, information, artifact, processing, etc.
- role of complexity theory in knowledge processing
- emphasize role of complex adaptive systems (CAS) in knowledge processing
- knowledge of complex adaptive systems (CAS) as a distinct form of knowledge or meta-knowledge
- 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
- can we ever really know what we know?
- beyond search: what is explicit search a special case of? e.g., seeking connections and opportunities
- role of anticipation and anticipatory influences in knowledge processing — e.g., role of beliefs about the future
- nature of curiosity and its role in knowledge processing
- nature and role of challenging assumptions in knowledge processing
- nature and role of emotions in knowledge processing
- nature and role of first movers in knowledge processing
- Orwell’s 1984: will CCKW and other knowledge processing approaches deter or abet Orwell’s gloomy scenario
- GPS (et al) position information: how will it affect knowledge processing
- affect of work on consumer knowledge processing
- roles of instant messaging, chat rooms, discussion forums, mailing lists, blogs, and podcasts in knowledge processing
- relationship and distinctions between ability/skills/aptitude and knowledge — are the former synonyms for tacit knowledge?
- unexpressed knowledge, inexpressible knowledge
- are tacit knowledge and implicit knowledge synonymous? if not, clarify the distinctions
- effectiveness of storytelling for conveying knowledge, but pros and cons for accuracy
- what might the impact of CCKW be on “The Knowledge Economy”?
- coping with rapidly changing knowledge
- knowledge sharing: as an explicit activity vs. implicit based on the CCKW knowledge infrastructure
- deterring and coping with corruption
- nature of knowledge communities and how to facilitate and support them
- debriefing: tacit knowledge download
- media content explicitly constructed to convey knowledge — e.g., audio and video clips and visuals
- exchanging (multi-directional interaction) as a method for transferring knowledge
- the critical issues of identifying consumer needs and interests
- the nature and roles of change agents and thought leaders — facilitating them, empowerment
- how to package knowledge: wide variety of approaches, need for further innovation
- the nature of informal knowledge networks: facilitating them
- nature and role of newsletters for knowledge processing
- nature of knowledge gaps and how they are filled
- limited attention span: nature and coping
- nature and role of illusion: pros and cons
- add PPT slides: 1) What is Knowledge? and 2) What is a Software Agent?
- need for a global storage grid for cheap, reliable, robust storage of knowledge virtual bits (knowledge bits)
- incentives and disincentives for knowledge sharing: protecting valid disincentives while promoting valid incentives
- impact of consumer culture on knowledge processing
- nature and role of critical analysis in knowledge processing
- nature and role of “the temple” vs. “the factory” in knowledge processing
- quantifying qualitative knowledge: pros and cons
- nature and role of bandwagons in knowledge processing
- the nature of nature as contrasted with behavior
- dissipation of knowledge: pros and cons, factors, attenuating
- nature and role of induction in knowledge processing
- nature and role of complex systems in knowledge processing
- nature and role of emergent order and disorderly systems in knowledge processing
- universals, range of any knowledge claim
- adages as a distinct form of knowledge
- stereotypes as a distinct form of knowledge
- prophesy as a distinct form of knowledge
- fairly comprehensive model of human nature as a key part of the CCKW knowledge infrastructure
- nature and role of absolutes and absoluteness in knowledge processing
- nature and role of personal morality in knowledge processing, building a personal morality knowledge structure
- sympathy and empathy as distinct forms of knowledge
- stimulants for knowledge processing: how to facilitate them
- retardants for knowledge processing: how to cope with and deter them
- how do groups learn (as opposed to individuals) and how is group knowledge different from individual knowledge
- nature and role of constructive non-conformity and creative tension in accelerating knowledge processing
- “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
- CCK: Codified Consumer Knowledge
- CIK: Codified Consumer Knowledge
- CGK: Codified Group Knowledge or Codified General Knowledge
- COK: Codified Organizational Knowledge
- CUK: Codified Universal Knowledge
- the knowledge claim as the key building block, the raw form of knowledge accepted as input to knowledge processing
- key goal of CCKW: eliminate the concept of search
- nature of distinctions between knowledge processing for consumers under CCKW and the consumer’s software agents under CCKW
- nature of supply and demand for knowledge processing
- nature of effective distribution of knowledge: facilitate it and measure it
- nature and role of mental models for knowledge processing
- support for identifying and coping with “worst ideas”
- nature and role of centrality: relative centers? relationship to context
- nature and role of human nature in knowledge processing — varying expression and influence
- nature and role of conceptual models in knowledge processing
- nature and role of entity identity — how do we ground references to things
- what should the characteristics of a robust “knowledge scenario” be? what impression should it leave?
- nature and role of thesauri in knowledge processing
- nature and role of confluence in knowledge processing — events, forces, motivations, influences, priorities
- if a picture is worth a thousand words, hw can we access the meaning of those words?
- web of meaning: original intent of Semantic Web, CCKW depends on it
- nature and role of feedback loops in knowledge processing
- nature and role of natural knowledge processes and rules in knowledge processing
- nature and role of goal sharing in knowledge processing
- nature and role of learning events in knowledge processing
- rule sets as a form for knowledge
- nature and role of declarative knowledge and procedural knowledge
- nature and role of knowledge as a response to stimuli in knowledge processing
- nature and role of knowledge production in knowledge processing
- interoperability of knowledge, especially with ontologies which are not strictly aligned
- nature and role of abstract concepts in knowledge processing
- nature and role of making sense in knowledge processing
- nature and role of learning and adapting in knowledge processing
- nature and role of coaching in knowledge processing
- nature and role of flames as distinct forms of knowledge
- 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)
- nature and role of simulation and modeling results and forecasts in knowledge processing
- constructing a science-based model of CCKW, including economics
- nature and role of anticipatory attention in knowledge processing
- nature and role of metadata — distinction from meaning of content itself
- nature and role of interpretation in knowledge processing
- nature and role of spimes in knowledge processing
- how do we test knowledge representations for their fidelity and comprehensibility?
- nature and role of gestures, facial expressions, and body language in knowledge processing
- nature and role of evocative knowledge objects (EKOs) in knowledge processing
- nature and role of theory objects in knowledge processing
- difficulties and strategies for coping with ambiguities concerning abstraction and instances
- 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
- still need to refine definition of knowledge
- nature and role of hunches in knowledge processing
- using software agents to transcend complexity
- nature and role of the attention economy in knowledge processing
- nature and role of writing styles and linguistic patterns in knowledge processing
- nature and role of multiple levels of meaning in knowledge processing
- nature and role of reader-specific contextual meaning in knowledge processing
- nature and role of private vocabularies in knowledge processing
- what can be gleaned from the FOAF Project (Friend Of A Friend)
- nature and role of the open-world assumption in knowledge processing
- nature and role of any inherent limitations of fidelity of representation of the real world in knowledge processing — not just the real world
- emphasize fundamental research
- nature and role of gaming and story-telling in knowledge processing — exploiting their full potential
- what might the topology of a knowledge web really look like? how can it be visualized?
- how to visualize knowledge relationships
- 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
- knowledge networking: what does the term really mean?
- nature and role of BrainJams in knowledge processing
- nature and role of Chautauquas in knowledge processing
- nature and role of knowledge silos in knowledge processing
- nature and role of the metaphor of the blind men and the elephant in knowledge processing
- approaches to reducing complexity
- knowledge schemas — extend from the information domain (since W3C has bastardized the concept of ontology anyway)
- community knowledge sharing and community knowledge systems (CKS) [Bobrow@PARC]
- sensemaking [PARC]
- nature and role of basic truths of nature in knowledge processing
- solving problems vs. coping with them
- grounded concept: conceptualize from data or grounded in data
- concept recognition: from data
- groundable concepts
- supporting concepts
- ontology: concepts, relations, and instances
- nature and role of topic ontology in knowledge processing
- relation of natural language grammar to knowledge
- structure of natural language
- social value of ambiguity
- nature and role of addressing overall consumer social needs in knowledge processing
- detail the ways in which Semantic Web is too weak for consumer-centric knowledge
- tacit understanding vs. tacit knowledge
- support mechanism for thinking, reasoning, understanding, challenging
- what aspects do we need to be cautious about, such as “the line” between computational and human intelligence and knowledge
- grade-level for knowledge: judging it, transforming to it
- nature and role of personalization — move to new levels (e.g., software agents to effect personalization)
- coping with skew and bias in searches
- nature and role of the distinctions between search and lookup
- CCKW as a distributed alternative to traditional search engines
- what is the unit of ignorance?
- nature of questions — role and as knowledge
- nature and role of numbers as knowledge
- mathematics and formulas as knowledge
- nature of being “lost”
- nature of the relation to the real world
- practical knowledge vs. theory knowledge
- structured natural language, pseudo natural language, pidgin natural language
- beyond direct access and abstract names — how to refer to things
- nature and role of pre-conditions and constraints
- expressing probabilistic qualities of knowledge
- faux banana example — nature of “like”
- nature and role of serendipity
- hard knowledge vs. soft knowledge
- soft vs. semi-hard knowledge
- software agents as “reach extenders”
- nature and role of creating new terms
- email: knowledge that is hibernating, stunned, at rest, resting
- knowledge “at rest” vs. form of intellectual energy
- tiered knowledge (levels)
- downsides and dangers of systemization
- the world is a mountainous jungle (and swamps and deserts and oceans) — not “flat” — stratification, silos
- value of diffuse knowledge
- essence of narrative — a key of knowledge
- nature and role of linking in narrative
- knowledge as a system — what is a system?
- cultural alignment
- Add to PPT: benefits to consumer
- middleware: “The Matrix”
- look for an ultra-simple disruptive angle
- culture-shift knowledge
- app: health-care delivery
- look for lifestyle angles
- the needs and interests of Millennials: information, entertainment, and social
- email: knowledge that is dying?
- stripes — disciplines, interests
- 5-D virtual environment for knowledge experience vs. 7-D or 12-D — what are the 5th and 6th dimensions
- consumers bring technology into work
- the consumer almighty
- nature of system vs. community
- nature and role of walled gardens
- software agents: psychology, social computing — moods — coping, representing, analyzing
- evolutionary learning
- evolutionary knowledge processing, construction
- basic map, geography, and geology knowledge
- basic geometry and spatial relationship knowledge
- GPS and RFID knowledge — as context and as knowledge itself
- basic general history and chronology knowledge
- basic government knowledge
- basic business knowledge
- basic biology, health-oriented, nutrition, and medical knowledge
- “best pizza” metaphor — difficulties
- knowledge requests vs. Q&A — research
- animated knowledge
- incremental disclosure knowledge
- quizzes as knowledge
- need a range of knowledge languages
- need a range of knowledge packaging
- imperatives as knowledge — who, where, and when
- nature and role of junk knowledge
- nature and role of subject vs. object — objective knowledge
- methods for “folding” knowledge
- GALS applied to knowledge
- Knowledge Factor
- what is a consumer really?
- word association as knowledge processing
- UML for knowledge modeling
- nature and role of “orbiting” in knowledge processing
- nature of acquiring knowledge
- consider a health focus for knowledge
- nature and role of placeholder objects
- nature of knowledge that solves a simple problem well
- relationships as knowledge
- concept aid
- nature and role of utopias
- collaborative environments
- icons and symbols as knowledge
- consider a roadmap “plan”
- elaborate on the Global Virtual Storage Network (extract from DVPC)
- PPT: emphasize why the consumer first
- PPT: consumer owns and controls their own knowledge
- nature and roles of levels of intelligence
- contemplate a “spreadsheet” for knowledge — Knowledge 123
- nature and role of spatial knowledge
- nature and role of temporal knowledge
- manifesto and requirements for a knowledge language
- list forms of knowledge — how open-ended is it
- list levels of knowledge — how open-ended is it
- list levels of expertness (relative to assumptions) — how open-ended is it
- need to address the basics of knowledge processing
- how to promote knowledge processing
- Latin as a neutral, common base for natural language knowledge processing — extended (modernized) Latin
- how to process knowledge represented in a non-Latin language
- future of “matching” — difficulty of alignment
- consider a pseudo-natural language for knowledge processing — not fully processable by machine yet, but “ready” for future AI software
- knowledge “depth” roadmap — text, soft, hard, flexible — “forms” of knowledge
- veneer of formalism — limits and benefits
- nature and role of anthropology in knowledge processing
- “The most important questions of life have never been and probably never will be formalized.”
- intuitive pragmatics
- VivoMind — John Sowa
- attention — belief — intention
- AI winter
- Internet Business Logic — executable open vocabulary English — www.reengineeringllc.com
- cost of ownership for knowledge
- cost of maintenance for knowledge
- meanings: extensional, intensional, pragmatic, and modal
- semiotic web vs. knowledge web vs. Semantic Web
- nature and role of ontological engineering — for fixed knowledge structures
- “relevant literature”
- SUO — Standard Upper Ontologies
- knowledge process for “the mobile world”
- philosophy web
- is plot related to meaning?
- ISO Common Logic
- Sowa FMF modules/components
- CLCE — Common Logic Controlled English
- nature and role of schools of thought
- USECS — substances and processes
- transfer learning
- reinforcement learning
- learning by example
- how knowledge can be structured
- school-independent knowledge models
- broader view of mechanisms for knowledge transfer
- nature and role of consumers as agents
- semantic grounded data sharing
- consider CCKW focused on hand-held mobile
- Machine Learning Technologies
- what are the issues for natural language processing?
- nature and role of feedback loops
- nature and role of proposals
- ontology reconciliation
- what is a better term for “ontology” as a specification for a domain?
- nature and role of propagation
- knowledge has pragmatics
- collaborative knowledge acquisition
- nature and role of state space
- the data exchange problem — distributed data
- agent management
- Amazon.com as a knowledge model
- Quine (1981) Theories and Things
- vaguer concepts which convey real meaning my virtue of common usage vs. pseudo-precise concepts
- nature and rol of intended model
- 43things as a social knowledge structuring mechanism
- DOLCE: Descriptive Ontology for Linguistic and Cognitive Engineering
- UFO: Universal Foundation Ontology or Unified Foundation Ontology — Harry Halpin, David Price
- hard facts vs. mushy text vs. application/domain-specific data packet
- ULO: Upper-Level Ontology
- bottom-up/emergent and lower-level foundation ontologies
- general meaning vs. specific meaning vs. contextual meaning
- nature and role of “reaching” — understanding, communicating
- learning through osmosis — simply being there and being attentive
- coherence vs. novelty — emergence
- “Ontology is composed of knowledge.”
- create a new natural language comprehensible to both people and computers
- a system and language analogous to LOGO but for traversing knowledge structures
- is language the issue or is context the issue?
- 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
- nature and role of bewilderment
- information overload caused by insufficient support for information selection, organization, and collaboration
- beyond P2P
- discovering promising partners, pool of cooperating partners
- nature and role of shallow vocabularies
- difficulties of manual translation, opportunities and benefits and limitations of semi-automated transformation
- nature and role of incompatible semantics
- IP: ontology vs. meaning
- nature and role of which information one believes
- nature and role of consequences of knowledge
- nature and role of commitment to particular knowledge
- emphasize evolution in knowledge and knowledge processing
- intelligence vs. knowledge processing