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Knowledge Representation, Reasoning, and the Design of Intelligent Agents
The Answer-Set Programming Approach

  • Date Published: May 2014
  • availability: Available
  • format: Hardback
  • isbn: 9781107029569


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About the Authors
  • Knowledge representation and reasoning is the foundation of artificial intelligence, declarative programming, and the design of knowledge-intensive software systems capable of performing intelligent tasks. Using logical and probabilistic formalisms based on answer set programming (ASP) and action languages, this book shows how knowledge-intensive systems can be given knowledge about the world and how it can be used to solve non-trivial computational problems. The authors maintain a balance between mathematical analysis and practical design of intelligent agents. All the concepts, such as answering queries, planning, diagnostics, and probabilistic reasoning, are illustrated by programs of ASP. The text can be used for AI-related undergraduate and graduate classes and by researchers who would like to learn more about ASP and knowledge representation.

    • In depth but highly accessible to the student audience
    • Covers both theory and practice, with implementations
    • Exercises, examples, historical notes
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    Reviews & endorsements

    'An excellent text for both students and experts in answer-set programming and knowledge representation.' Chitta Baral, Arizona State University

    'Michael Gelfond is one of the creators of answer-set programming, a new programming methodology based on artificial intelligence that has already found several important applications. I am extremely impressed by the clarity of thought and examples provided. The authors are to be congratulated on this excellent addition to the literature.' Vladimir Lifschitz, University of Texas, Austin

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    Product details

    • Date Published: May 2014
    • format: Hardback
    • isbn: 9781107029569
    • length: 360 pages
    • dimensions: 235 x 157 x 23 mm
    • weight: 0.61kg
    • contains: 25 b/w illus. 3 tables 97 exercises
    • availability: Available
  • Table of Contents

    1. Logic-based approach to agent design
    2. Answer set Prolog (ASP)
    3. Roots of ASP
    4. Creating a knowledge base
    5. Representing defaults
    6. The answer set programming paradigm
    7. Algorithms for computing answer sets
    8. Modeling dynamic domains
    9. Planning agents
    10. Diagnostic agents
    11. Probabilistic reasoning
    12. The Prolog programming language.

  • Resources for

    Knowledge Representation, Reasoning, and the Design of Intelligent Agents

    Michael Gelfond, Yulia Kahl

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  • Authors

    Michael Gelfond, Texas Tech University
    Dr Michael Gelfond is a Professor of Computer Science at Texas Tech University. He received his PhD from the Steklov Institute of Mathematics of the Academy of Sciences, St Petersburg, Russia. He is an AAA Fellow and serves as an Area Editor for the International Journal of Theory and Practice of Logic Programming and as an Executive Editor of the Journal of Logic and Computation. In 2004 and 2012 he was the recipient of the award for most influential paper in twenty years by the International Association of Logic Programming.

    Yulia Kahl
    Yulia Kahl is a member of the Texas Action Group and the Knowledge Representation Lab at Texas Tech. She received her Master's in Computer Science, focusing on the use of ASP for planning. She has also worked as a programmer at IBM.

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