DISTRIBUTED BAYESIAN NETWORKS IN HIGHLY DYNAMIC AGENT ORGANIZATIONS

In this paper we focus on the problems associated with distributed approaches to exact belief propagation in multi agent systems. In particular, we discuss the Multiply Sectioned Bayesian networks (MSBN) and Distributed Perception Networks (DPNs). While MSBNs support modeling of more complex domains than DPNs, we argue that MSBN approach is not suitable for large and changing agent societies. DPNs, on the other hand, are a special class of MSBNs and have limited modeling capabilities. However, DPN approach facilitates selforganization of distributed systems and, consequently, can cope with variable agent societies.