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Hector J. Levesque

Hector J. Levesque is Professor Emeritus in the Department of Computer Science at the University of Toronto. He is the author of Common Sense, the Turing Test, and the Quest for Real AI, coauthor (with Gerhard Lakemeyer) of The Logic of Knowledge Bases, and coeditor (with Ronald J. Brachman) of Knowledge Representation and Reasoning, all three published by the MIT Press.

Titles by This Author

What can artificial intelligence teach us about the mind? If AI’s underlying concept is that thinking is a computational process, then how can computation illuminate thinking? It’s a timely question. AI is all the rage, and the buzziest AI buzz surrounds adaptive machine learning: computer systems that learn intelligent behavior from massive amounts of data. This is what powers a driverless car, for example.

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 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.

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.Ronald J.