Probabilistic Reasoning in Multiagent Systems - Yang Xiang.pdf

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PROBABILISTIC REASONING IN

MULTIAGENT SYSTEMS

This book investigates the opportunities in building intelligent decision support

systems offered by multiagent 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 past two decades. The suc-

cess of this technique in modeling intelligent decision support systems under the

centralized and single-agent paradigm has been striking. In this book, the author ex-

tends graphical dependence models to the distributed and multiagent 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 un-

certain knowledge on a large and complex environment embedded in multiple

cooperative agents and effective, exact, and distributed probabilistic inference.

Yang Xiang is Associate Professor of Computing and Information Science at the

University of Guelph, Canada, where he directs the Intelligent Decision Support

System Laboratory. He received his Ph.D. from the University of British Columbia

and developed the Java-based toolkit WebWeavr, which has been distributed to

registered users in more than 20 countries. He also serves as Principal Investigator

in the Institute of Robotics and Intelligent Systems (IRIS), Canada.

PROBABILISTIC REASONING IN

MULTIAGENT SYSTEMS

A Graphical Models Approach

YANG XIANG

University of Guelph