Knowledge Representation: An AI Perspective
Most researchers to date in artificial intelligence has been based on the knowledge representation hypothesis, that is, the assumption that in any artificial intelligence (AI) programme there is a separate module which represents the information that the programme has about the world. As a result, a number of so-called knowlege representation formalisms have been developed for representing this kind of information in a computer.
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Some General Problems in Knowledge Representation
Logicbased Knowledge Representation Languages
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activation value algorithm apply arguments associated Brachman called Chapter class frame cognitive concept conflict set connection connectionism connectionist control regime corresponding default and nonmonotonic default logic default reasoning defined delta rule derive determine discuss elephant entities example existentially quantified expert systems fact first-order predicate calculus FOPC frame-based representation heuristics hidden units hypothesis implemented individual inference procedure inference rules inheritance input instance frame interpreter KL-ONE knowledge base knowledge representation language KRYPTON learning logic-based knowledge representation match meaning memory elements modus ponens MYCIN neuron nodes nonmonotonic logics object output particular past tenses pattern PDP approach PDP models piece of information Pinker and Prince possible predicate primitive problem procedural representations production rule systems proof theory proposed propositions represent restrictions rule base Rumelhart Rumelhart and McClelland semantic nets semantic networks slot SNePS solve specific stored strategic knowledge structure subgoals superclass TBox theorem prover tion true Tweety variables