Knowledge Representation (Special Issues of Artificial Intelligence)

Growing interest in symbolic representation and reasoning has pushed this backstage activity into the spotlight as a clearly identifiable and technically rich subfield in artificial intelligence. This collection of extended versions of 12 papers from the First International Conference on Principles of Knowledge Representation and Reasoning provides a snapshot of the best current work in AI on formal methods and principles of representation and reasoning. The topics range 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 Bell Laboratories. Hector J. Levesque and Raymond Reiter are Professors of Computer Science at the University of Toronto.Contents: Introduction. Nonmonotonic Reasoning in the Framework of Situation Calculus. The Computational Complexity of Abduction. Temporal Constraint Networks. Impediments to Universal Preference-Based Default Theories. Embedding Decision-Analytic Control in a Learning Architecture. The Substitutional Framework for Sorted Deduction: Fundamental Results on Hybrid Reasoning. Existence Assumptions in Knowledge Representation. Hard Problems for Simple Default Logics. The Effect of Knowledge on Belief: Conditioning, Specificity and the Lottery Paradox in Default Reasoning. Three-Valued Nonmonotonic Formalisms and Semantics of Logic Programs. On the Applicability of Nonmonotonic Logic to Formal Reasoning in Continuous Time. Principles of Metareasoning. This text comprises extended versions of 12 papers from the First International Conference on Principles of Knowledge Representation and Reasoning. Topics covered include temporal reasoning, default reasoning, and representations for natural language