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 success of this technique in modeling intelligent decision support systems under the centralized and singe-agent paradigm has been striking. In this book, the author extends 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 uncertain knowledge on a large and complex environment embedded in multiple cooperative agents and effective, exact, and distributed probabilistic inference.

About the Author

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.