[XML-DEV Mailing List Archive Home] [By Thread] [By Date] [Recent Entries] [Reply To This Message] RE: Beyond Ontologies
I agree about the expense, but in studying the web as a system, it is useful to forgo the technical web architecture and come to understand that its complexity emerges from the interaction of agents both human and automated, and for the semantic web future, agents empowered to act on behalf of human owners, thus The Golem Problem I discussed here and in the Markup Languages MIT magazine article. Asking isn't the whole solution, but even in agent-based modeling, it is an important task. For many kinds of conflicts (say the human profile), it is the only appropriate place to start. The toughest problem of agent-based modeling is not knowing what the human owner wants, and worse, that the human does not know what they want. They act on habit formation which might be called 'instinct'. Understanding habit formation with regards to symbol/sign assignment is important to finding the conflict of semantics. The interface aspects are VERY important and why marketing designers and for their own good, web GUI designers, should study semiotics. And so should we because semiotics are important to how and why web systems become complex. Here are some notes from the article you reference for others who are only lightly following this thread, to illuminate the topics, edited for brevity so not exactly quoted. Comments in brackets are mine. I start with the Eurobios site definitions: Also from Eurobios site: Complex Systems: elaborate and unpredictable properties arise from interacting agents. Examples of emergent properties include how the system organizes itself, how it finds balance of order and disorder, how agents individually and collectively evolve new behaviors in response to change. Emergent behavior: emerges from interactions with other agents and environment rather than being imposed because behavior cannot be deduced from the rules. Co-adaptation: actions of all agents in environment affect each other, such that they co-adapt and co-evolve. Competitive advantage is gained by effectively adapting to novel and unpredictable situations faster than the competition (seeing the setup). Then the Economist article: "Complex Agent-Based Dynamic Network: explain behaviour through the use of agents: a program that acts in a self-interested manner in its dealings with numerous other agents inside a computer. Can mimic almost any interactive system. If the constituent parts can be understood, some insight into the whole will follow. [The goal is prediction. Simulation or role play outs the intent of the communication: what the agent wants. Hint: what are the public and private couplers?] One of the challenges in setting up an agent-based model is defining clearly what individual agents want. Not only are people often inaccurate in their beliefs about themselves, but especially in the business world they lie. [Measure]: she set out to measure what was actually going on. Spent hours watching and recording which machines were running when. [See Fisher Information] [Entropy as measure of system disorganization] Compared her findings about what was really happening with what people claimed was happening on paperwork such as invoices. By checking how often these agreed, she could approximate the mathematical entropy of the system: a measure of how disordered it was. [Entropy as measure of real time knowledge. Compare Boltzman and Shannon entropies and the concept of addressing. Why URIs and the problem of URI management] A complicated factory, with many different assembly lines, can still be ordered if everything is proceeding according to plan. Even a simple one, by contrast, would be disordered if managers had no idea of what was happening." len From: Didier PH Martin [mailto:martind@n...] Practically, since we tried that in the past, asking to humans to resolve issues is long and costly process. Moreover, I may have some difficulties to ask the others to resolve all ontological conflicts I will encounter on the web. However, I think, intuitively, you point to a good direction. More and more I think that agents based modeling can help a lot for this type of phenomenon hard to model the usual way. For those of us who want to explore the agents based modeling here is a good link. http://www.econ.iastate.edu/tesfatsi/ace.htm. It's about using agents based modeling in the context of economics but it could be very well applied on the topic of concept categorization or concept clustering. The site links to a lot of articles with a pedagogical perspective. I think that agents based modeling can be tremendously useful for a _pratical_ semantic web.
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