Anytime algorithms for multiagent decision making using coordination graphs

Coordination graphs provide a tractable framework for cooperative multiagent decision making by decomposing the global pay off function into a sum of local terms. In this paper we review some distributed algorithms for action selection in a coordination graph and discuss their pros and cons. For real-time decision making we emphasize the need for anytime algorithms for action selection: these are algorithms that improve the quality of the solution over time. We describe variable elimination, coordinate ascent, and the max-plus algorithm, the latter being an instance of the belief propagation algorithm in Bayesian networks. We discuss some interesting open problems related to the use of the max-plus algorithm in real-time multiagent decision making