Modeling Agent Systems by Bayesian Belief Networks

Abstract

Development of multi-agent system (MAS) applications is often complicated by the fact that agents operate in a dynamic, uncertain world. Uncertainty may stem from noisy external data, inexact reasoning such as abduction, and actions by individual agents. Uncertainty can be compounded and amplified when propagated through the agent system. Moreover, some agents may become disconnected from the rest of the system by temporary or permanent disability of these agents or their communication channel, resulting in incomplete/inconsistent system states. How should we represent individual agents acting in such an uncertain environment, and more importantly, how can we predict how the MAS as a whole will evolve as the result of uncertain inter-agent interactions? These questions cannot be correctly answered without a correct agent interaction model based on a solid mathematical foundation.