Skip navigation

Hector J. Levesque

Hector J. Levesque is Professor of Computer Science at the University of Toronto. He is the coauthor (with Gerhard Lakemeyer) of The Logic of Knowledge Bases (MIT Press, 2001) and coeditor (with Ronald J. Brachman) of Knowledge Representation and Reasoning (MIT Press, 1992).

Titles by This Author

A First Course

This book guides students through an exploration of the idea that thinking might be understood as a form of computation. Students make the connection between thinking and computing by learning to write computer programs for a variety of tasks that require thought, including solving puzzles, understanding natural language, recognizing objects in visual scenes, planning courses of action, and playing strategic games. The material is presented with minimal technicalities and is accessible to undergraduate students with no specialized knowledge or technical background beyond high school mathematics. Students use Prolog (without having to learn algorithms: “Prolog without tears!”), learning to express what they need as a Prolog program and letting Prolog search for answers.

After an introduction to the basic concepts, Thinking as Computation offers three chapters on Prolog, covering back-chaining, programs and queries, and how to write the sorts of Prolog programs used in the book. The book follows this with case studies of tasks that appear to require thought, then looks beyond Prolog to consider learning, explaining, and propositional reasoning. Most of the chapters conclude with short bibliographic notes and exercises. The book is based on a popular course at the University of Toronto and can be used in a variety of classroom contexts, by students ranging from first-year liberal arts undergraduates to more technically advanced computer science students.

The idea of knowledge bases lies at the heart of symbolic, or "traditional," artificial intelligence. A knowledge-based system decides how to act by running formal reasoning procedures over a body of explicitly represented knowledge—a knowledge base. The system is not programmed for specific tasks; rather, it is told what it needs to know and expected to infer the rest.

This book is about the logic of such knowledge bases. It describes in detail the relationship between symbolic representations of knowledge and abstract states of knowledge, exploring along the way the foundations of knowledge, knowledge bases, knowledge-based systems, and knowledge representation and reasoning. Assuming some familiarity with first-order predicate logic, the book offers a new mathematical model of knowledge that is general and expressive yet more workable in practice than previous models. The book presents a style of semantic argument and formal analysis that would be cumbersome or completely impractical with other approaches. It also shows how to treat a knowledge base as an abstract data type, completely specified in an abstract way by the knowledge-level operations defined over it.

Titles by This Editor

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.

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.