Probabilistic Reasoning in Multiagent Systems

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.

• Author is pre-eminent authority on the subject, and initiated the research on the framework presented in this book
• Comprehensive book that addresses subject of probabilistic inference by multiple agents using graphical knowledge representations
• Multi-agent systems will be important in the future due to the cost of reduction of computers and networking