Bayesian probability theory provides a rigorous foundation for building intelligent agents that must reason in stochastic environments. To meet the computational challenge, significant progresses have been made through centralized graphical modeling. As problems of larger scale and higher complexity are being tackled, multiagent systems promise a new paradigm of divide-and-conquer, but also raise new issues on how Bayesian reasoning can be conducted in such a context.

This talk focuses on multiagent Bayesian reasoning through graphical models. The computational challenge of probabilistic reasoning and how Bayesian networks meet the challenge will be reviewed. After motivating multiagent systems, multiply sectioned Bayesian networks (MSBNs) will be introduced as a class of graphical models for multiagent uncertain knowledge representation. The fundamental assumptions that logically lead to MSBNs will be discussed. How multiple agents reason probabilistically using MSBNs will be presented algorithmically, and key computational properties of the framework will be discussed. The computation process will be illustrated through equipment monitoring and fault isolation.