Probabilistic Reasoning in Multiagent Systems A Graphical Models Approach [Paperback]

Addresses the challenges of building intelligent agents to cooperate on complex tasks in uncertain environments.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.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.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 distrl³$