ABSTRACT

This project develops a decision-theoretic framework for planning and control of multi-agent systems by formalizing the problem as decentralized Markov process. It applies to a wide range of application domains in which decision-making must be performed by multiple collaborating agents such as information gathering, distributed sensing, coordination of multiple robots, as well as the operation of complex human organizations. While substantial progress has been made in planning and control of single agents using MDPs, a similar formal treatment of multi-agent systems has been lacking. Existing techniques tend to avoid a central issue: agents typically have different information about the overall system and they cannot share all this information all the time. Sharing information has a cost that must be factored into the overall decision process. Three approaches to communication are studied based on (1) a cost/benefit analysis of the amount of communication, (2) search in policy space, and (3) transformations of the more tractable centralized policies into decentralized policies. The resulting techniques are evaluated in the context of several realistic applications. This research facilitates a better understanding of the strengths and limitations of existing heuristic approaches to coordination and, more importantly, it includes new approaches based on more formal underpinnings.

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