A fundamental challenge in artificial intelligence is to achieve intelligent coordination of a group of decision makers in spite of uncertainty and limited information. Decision theory offers a normative framework for optimizing decisions under uncertainty, but due to computational barriers, developing decision-theoretic reasoning algorithms for multi-agent systems is a serious challenge. This project will advance foundational contributions to the understanding of decision-theoretic planning in stochastic multi-agent domains as well as the development of efficient new algorithms that provide exponential savings in memory requirements and computation time. Moving beyond toy problems is a hard computational challenge that has been broadly recognized by the multiagent systems community. Research under this project will transform the ability of researcher and practitioners to apply decision-theoretic planing to a new range of domains.
