Probabilistic Reasoning in Multiagent Systems: A Graphical Models Approach

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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.

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Editorial Reviews

From the Publisher
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

From The Critics
Investigating the use of Bayesian networks or belief networks for building intelligent decision support systems, Xiang (computing and information science, U. of Guelph, Canada) extends the application of these graphical dependence models from the centralized and single- agent paradigm to representation formalisms under the distributed and multiagent paradigm. After identifying the technical challenges to such an application, he presents his research on the matter. The foci of the work is the structuring of multiple agents' knowledge as a set of probabilistic graphical models, the compilation of the models into graphical structures for message passing, and the use of message passing to accomplish tasks in model verification and compilation and distributed interference. Annotation c. Book News, Inc., Portland, OR
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Product Details

  • ISBN-13: 9780521813082
  • Publisher: Cambridge University Press
  • Publication date: 9/28/2010
  • Pages: 308
  • Product dimensions: 6.85 (w) x 9.72 (h) x 0.75 (d)

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.

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