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Probabilistic Reasoning in Multiagent Systems

Probabilistic Reasoning in Multiagent Systems
A Graphical Models Approach

$151.00 (C)

  • Date Published: August 2002
  • availability: Available
  • format: Hardback
  • isbn: 9780521813082

$ 151.00 (C)
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About the Authors
  • Probalistic reasoning with graphical models, also known as Bayesian networks or belief networks, has become an active field of research and practice in artifical intelligence, operations research and statistics in the last two decades. The success of this technique in modeling intelligent decision support systems under the centralized and single-agent paradim has been striking. In this book, the author extends graphical dependence models to the distributed and multi-agent paradigm. He identifies the major technical challenges involved in such an endeavor and presents the results gleaned from a decade's research.

    • Author is pre-eminent authority on the subject, and initiated the research on the framework presented in this book
    • Comprehensive book that addresses subject of probabilistic inference by multiple agents using graphical knowledge representations
    • Multi-agent systems will be important in the future due to the cost of reduction of computers and networking
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    Reviews & endorsements

    Review of the hardback: '… this is a valuable and welcome comprehensive guide to the state-of-the-art in applying belief networks.' Kybernetes

    Review of the hardback: '… the well-balanced treatment of multiagent systems will make the book useful to both theoretical computer scientists and the more applied artificial intelligence community. Moreover, the interdisciplinary nature of the subject makes it relevant not only to computer scientists but also to people from operations research and microeconomics (social choice and game theory in particular). The book easily deserves to be on the shelf of any modern theoretical computer scientist.' SIGACT News

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    Product details

    • Date Published: August 2002
    • format: Hardback
    • isbn: 9780521813082
    • length: 308 pages
    • dimensions: 254 x 178 x 19 mm
    • weight: 0.75kg
    • availability: Available
  • Table of Contents

    Preface
    1. Introduction
    2. Bayesian networks
    3. Belief updating and cluster graphs
    4. Junction tree representation
    5. Belief updating with junction trees
    6. Multiply sectioned Bayesian networks
    7. Linked junction forests
    8. Distributed multi-agent inference
    9. Model construction and verification
    10. Looking into the future
    Bibliography
    Index.

  • Author

    Yang Xiang, University of Guelph, Ontario

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