Probabilistic Reasoning in Multiagent Systems: A Graphical Models Approach

Yang Xiang

Published by Cambridge University Press, 2009
ISBN 10: 0511546939 / ISBN 13: 9780511546938
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Synopsis: 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.

Book Description: To maintain today's ever-more-complex computer systems, there is a growing need for intelligent agents that can cooperate on complex tasks in an uncertain environment. In applications as diverse as equipment diagnosis, engineering design, sensor networks, area monitoring, and situation assessment, such agents must be able to use limited information and observatins to assess the state of their environment and take appropriate actions. This book identifies the technical challenges in building intelligent agents and provides a rigorous framework for meeting these challenges. It is the first book that addresses the subject of probabilistic inference by multiple agents using graphical knowledge representations.

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Title: Probabilistic Reasoning in Multiagent ...
Publisher: Cambridge University Press
Publication Date: 2009
Binding: Printed Access Code
Book Condition: Used: Good

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Book Description CAMBRIDGE UNIVERSITY PRESS, United Kingdom, 2010. Paperback. Book Condition: New. Language: English . Brand New Book ***** Print on Demand *****. This 2002 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 increasingly an active field of research and practice in artificial intelligence, operations research and statistics. The success of this technique in modeling intelligent decision support systems under the centralized and single-agent paradigm has been striking. Yang Xiang 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. 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. Bookseller Inventory # AAV9780521153904

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