Heuristic search for identical payoff Bayesian games

Bayesian games can be used to model single-shot decision problems in which agents only possess incomplete informa-tion about other agents, and hence are important for mul-tiagent coordination under uncertainty. Moreover they can be used to represent different stages of sequential multia-gent decision problems, such as POSGs and DEC-POMDPs, and appear as an operation in many methods for multiagent planning under uncertainty. In this paper we are interested in coordinating teams of cooperative agents. While many such problems can be formulated as Bayesian games with identical payoffs, little work has been done to improve solu-tion methods. To help address this situation, we provide a branch and bound algorithm that optimally solves identical payoff Bayesian games. Our results show a marked improve-ment over previous methods, obtaining speedups of up to 3 orders of magnitude for synthetic random games, and reach-ing 10 orders of magnitude speedups for games in a DEC-POMDP context. This not only allows Bayesian games to be solved more efficiently, but can also improve multiagent planning techniques such as top-down and bottom-up algo-rithms for decentralized POMDPs