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.

  • Common Sense, the Turing Test, and the Quest for Real AI

    Common Sense, the Turing Test, and the Quest for Real AI

    Hector J. Levesque

    What artificial intelligence can tell us about the mind and intelligent behavior.

    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. In this book, Hector Levesque shifts the conversation to “good old fashioned artificial intelligence,” which is based not on heaps of data but on understanding commonsense intelligence. This kind of artificial intelligence is equipped to handle situations that depart from previous patterns—as we do in real life, when, for example, we encounter a washed-out bridge or when the barista informs us there's no more soy milk.

    Levesque considers the role of language in learning. He argues that a computer program that passes the famous Turing Test could be a mindless zombie, and he proposes another way to test for intelligence—the Winograd Schema Test, developed by Levesque and his colleagues. “If our goal is to understand intelligent behavior, we had better understand the difference between making it and faking it,” he observes. He identifies a possible mechanism behind common sense and the capacity to call on background knowledge: the ability to represent objects of thought symbolically. As AI migrates more and more into everyday life, we should worry if systems without common sense are making decisions where common sense is needed.

    • Hardcover $23.95 £19.95
    • Paperback $19.95 £14.99
  • Thinking as Computation

    Thinking as Computation

    A First Course

    Hector J. Levesque

    Students explore the idea that thinking is a form of computation by learning to write simple computer programs for tasks that require thought.

    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.

    • Hardcover $55.00 £43.00
    • Paperback $30.00 £24.00
  • The Logic of Knowledge Bases

    The Logic of Knowledge Bases

    Hector J. Levesque and Gerhard Lakemeyer

    This book 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.

    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.

    • Hardcover $11.75 £9.99
    • Paperback $42.00 £33.00