[XML-DEV Mailing List Archive Home] [By Thread] [By Date] [Recent Entries] [Reply To This Message] RE: First Order Logic and Semantic Web RE: NPR, Godel, Semantic W eb
Yes, the problems of amplification and catastrophe in a feedback system: well, essentially, at onset, you have to *feel* it and put your palm on the strings before the speakers blow... ;-) (the answer is in the feedback formula; the control or policy for returning output to input). Let me ask you this, how does a human negotiate for a used car? In other words, many contracts start out with only a minimal amount of trust among the partners in the transaction. Ask yourself in any trading situation what procedures or tasks do you do to ensure the situation meets your needs. How do you express those needs to a potential partner? I see these as separate issues: logical procedures for negotiating a basis for trust, maintaining a private registry of trusted partners, creating a trustworthy knowledge base. How does the Survivor game on TV work (never watch it myself - degrading)? I should think one would look at the UDDI/WSDL service model and find the place where the ontology fits. What service is it providing? As to **how does one train an agent**, I should think that the critical question. See DAML. What is the agent allowed to DO? Get to that first. How do we constrain human agents? Protocol, policy, backups, reviews, etc. I submit one has to look very hard at negotiation in contexts of policy and opportunism. Style counts for humans. For SW? It depends on just how complex a logical layer you want to devise, the kinds of agents, how much analogical reasoning you enable, etc. If you want a thought experiment, the hottest domain for research at the moment is using an avatar or virtual human interface as the GUI. What would you need to make that believable (not real, but believable in the sense that you know Bugs Bunny is not real, but he is believable)? Building the knowledge base, as hard as it looks today, is probably tedious but easier than what follows. After that, the layer that enables the agent semi-autonomous capacity to evolve a strategy in moreorless real time is the hard part. It is a problem similar if not identical to the problems of interactive fiction and believable characters (which is why some of us work in that field - fun, artsy, and illuminating). So good question: how does one train an agent? Well, first the agent needs memory, both of specific facts and what was once called, episodic memory so it has the capacity to work with stereotypes and match reactions to events (feel it; put palm on strings). If a stereotype is identified, how can it avoid falling into local minima? Annealing was once a topic of discussion in that context. But before we get that deep, basic WSDL, routing of application data to application, transforms, etc. Most of the business documents and business logic are tested long before you commit a mission critical operation to them. The applications in those domains are actually unlikely to be as open as the web. That is the flaw in open vs closed system assumptions. There is a middle ground (the keiretsu) in which the operational chain is defined by contract, tested, and known. It is closed in the sense that expectations are defined and tested prior to committing resources to it, so it is not chaotically seeking patterns; it is opportunistic. Len http://www.mp3.com/LenBullard Ekam sat.h, Vipraah bahudhaa vadanti. Daamyata. Datta. Dayadhvam.h -----Original Message----- From: Jeff Lowery [mailto:jlowery@s...] How does one train an agent?
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