Knowledge Representation

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Ronald J. Brachman, Hector J. Levesque, Raymond Reiter
MIT Press, 1992 - Computers - 408 pages
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Growing interest in symbolic representation and reasoning has pushed this backstageactivity into the spotlight as a clearly identifiable and technically rich subfield in artificialintelligence. This collection of extended versions of 12 papers from the First InternationalConference on Principles of Knowledge Representation and Reasoning provides a snapshot of the bestcurrent work in AI on formal methods and principles of representation and reasoning. The topicsrange from temporal reasoning to default reasoning to representations for natural language.Ronald J.Brachman is Head of the Artificial Intelligence Principles Research Department at AT&T BellLaboratories. Hector J. Levesque and Raymond Reiter are Professors of Computer Science at theUniversity of Toronto.Contents: Introduction. Nonmonotonic Reasoning in the Framework of SituationCalculus. The Computational Complexity of Abduction. Temporal Constraint Networks. Impediments toUniversal Preference-Based Default Theories. Embedding Decision-Analytic Control in a LearningArchitecture. The Substitutional Framework for Sorted Deduction: Fundamental Results on HybridReasoning. Existence Assumptions in Knowledge Representation. Hard Problems for Simple DefaultLogics. The Effect of Knowledge on Belief: Conditioning, Specificity and the Lottery Paradox inDefault Reasoning. Three-Valued Nonmonotonic Formalisms and Semantics of Logic Programs. On theApplicability of Nonmonotonic Logic to Formal Reasoning in Continuous Time. Principles ofMetareasoning.


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The Logic of Knowledge Bases

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About the author (1992)

Raymond Reiter is Professor and Co-Director of the Cognitive Robotics Project in the Department of Computer Science at the University of Toronto.