YKY, this kind of thing has been tried many dozens of times in the history of AI.
It does not lead to interesting results! Alas... The key problem is that you can't feasibly encode enough facts to allow interesting commonsense inferences -- commonsense inference seems to require a very massive store of highly uncertain knowledge-items, rather than a small store of certain ones. BTW the rule "if X is-a Y and Z(Y), then Z(X)". exists (in a slightly different form) in Novamente and many other inference systems... I feel like you are personally rediscovering GOFAI, the kind of AI that I read about in textbooks when I first started exploring the field in the early 1980's!!!! Ben G
Thanks for the tips. My idea is quite simple, slightly innovative, but not groundbreaking. Basically, I want to collect a knowledgebase of facts as well as rules. Facts are like "water is wet" etc. The rules I explain with this example: "Cats have claws; Kitty is a cat; therefore Kitty has claws." Here is an implicit rule that says "if X is-a Y and Z(Y), then Z(X)". I call rules like this the "Rules of Thought". They are not logical tautologies but they express some common thought patterns. My theory is that if we collect a bunch of these rules, add a database of common sense facts, and add a rule-based FOPL inference engine (which may be enhanced with eg Pei Wang's numerical logic), then we have a common sense reasoner. That's what I'm trying to build as a first-stage AGI. If it does work, there may be some commercial applications for such a reasoner. Also it would serve as the base to build a full AGI capable of machine learning etc (I have crudely worked out the long-term plan). So, is this a good business idea? YKY ________________________________ This list is sponsored by AGIRI: http://www.agiri.org/email To unsubscribe or change your options, please go to: http://v2.listbox.com/member/?list_id=303
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