Bayesian Agent Adaptation in Complex Dynamic Systems

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Author(s): Mair Allen-Williams (University of Southampton, UK)and Nicholas R. Jennings (University of Southampton, UK)
Copyright: 2010
Pages: 37
Source title: Handbook of Research on Complex Dynamic Process Management: Techniques for Adaptability in Turbulent Environments
Source Author(s)/Editor(s): Minhong Wang (University of Hong Kong, Hong Kong)and Zhaohao Sun (University Of Ballarat, Australia )
DOI: 10.4018/978-1-60566-669-3.ch007

Keywords: Business & Management / Business Information Systems / Business Process Management / Business Science Reference

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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.

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