Bayesian Agent Adaptation in Complex Dynamic Systems

Abstract

Multi-agent systems draw together a number of significant trends in modern technology: ubiquity, decentralization, openness, dynamism and uncertainty. As work in these fields develops, such systems face increasing challenges. Two particular challenges are decision making in uncertain and partiallyobservable environments, and coordination with other agents in such environments. Although uncertainty and coordination have been tackled as separate problems, formal models for an integrated approach are typically restricted to simple classes of problem and are not scalable to problems with many agents and millions of states. We improve on these approaches by extending a principled Bayesian model into more challenging domains, using heuristics and exploiting domain knowledge in order to make approximate solutions tractable. We show the effectiveness of our approach applied to an ambulance coordination problem inspired by the Robocup Rescue system.