Probabilistic Reasoning in Multiagent Systems

A Graphical Models Approach

Yang Xiang author

Format:Paperback

Publisher:Cambridge University Press

Published:24th Jun '10

Currently unavailable, and unfortunately no date known when it will be back

This paperback is available in another edition too:

Probabilistic Reasoning in Multiagent Systems cover

Addresses the challenges of building intelligent agents to cooperate on complex tasks in uncertain environments.

This 2002 book identifies the technical challenges in building intelligent agents that can cooperate on complex tasks in an uncertain environment and provides a rigorous framework for meeting these challenges. It is a comprehensive book that addresses the subject of probabilistic inference by multiple agents using graphical knowledge representations.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.

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

ISBN: 9780521153904

Dimensions: 244mm x 170mm x 16mm

Weight: 490g

308 pages