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Probabilistic Reasoning in Multiagent Systems: A Graphical Models Approach

Probabilistic Reasoning in Multiagent Systems: A Graphical Models Approach

Probabilistic Reasoning in Multiagent Systems: A Graphical Models Approach
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Probabilistic Reasoning in Multiagent Systems: A Graphical Models Approach Hardback - 2002

by Xiang, Yang

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Cambridge University Press. Used - Like New. Page block firm and clean, binding unblemished, boards straight, no markings of any kind. Fine, like new condition. Well packaged and promptly shipped from California. Partnered with Friends of the Library since 2010.
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Details

  • Title Probabilistic Reasoning in Multiagent Systems: A Graphical Models Approach
  • Author Xiang, Yang
  • Binding Hardback
  • Edition 1st
  • Condition New
  • Pages 308
  • Volumes 1
  • Language ENG
  • Publisher Cambridge University Press
  • Publication date 2002-08-26
  • Illustrated Yes
  • Features Bibliography, Illustrated, Index, Table of Contents
  • Bookseller's Inventory # BAY_11_SH_022686
  • ISBN 9780521813082 / 0521813085
  • Weight 1.67 lbs (0.76 kg)
  • Dimensions 9.98 x 7.08 x 0.93 in (25.35 x 17.98 x 2.36 cm)
  • Category Computers - General Information
  • Library of Congress subjects Intelligent agents (Computer software), Bayesian statistical decision theory - Data
  • Library of Congress Catalogue Number 2001052874
  • Dewey Decimal Code 006.3
  • Quantity available 1

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Summary

This book investigates the opportunities in building intelligent decision support systems offered by multi-agent distributed probabilistic reasoning. Probabilistic reasoning with graphical models, also known as Bayesian networks or belief networks, has become an active field of research and practice in artificial 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 paradigm 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 from a decade's research. The framework developed in the book allows distributed representation of uncertain knowledge on a large and complex environment embedded in multiple cooperative agents, and effective, exact and distributed probabilistic inference.

Reader reviews for Probabilistic Reasoning in Multiagent Systems: A Graphical Models Approach

From the publisher

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.

First line

An intelligent agent is a computational or natural system that senses its environment and takes actions intelligently according to its goals.
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