The Complexity of Decentralized Control of Markov Decision Processes
UAI '00 Proceedings of the 16th Conference on Uncertainty in Artificial Intelligence
Approximate Solutions for Partially Observable Stochastic Games with Common Payoffs
AAMAS '04 Proceedings of the Third International Joint Conference on Autonomous Agents and Multiagent Systems - Volume 1
Sequential decision making in repeated coalition formation under uncertainty
Proceedings of the 7th international joint conference on Autonomous agents and multiagent systems - Volume 1
Formal models and algorithms for decentralized decision making under uncertainty
Autonomous Agents and Multi-Agent Systems
Point-based incremental pruning heuristic for solving finite-horizon DEC-POMDPs
Proceedings of The 8th International Conference on Autonomous Agents and Multiagent Systems - Volume 1
Dynamic programming for partially observable stochastic games
AAAI'04 Proceedings of the 19th national conference on Artifical intelligence
Point-based dynamic programming for DEC-POMDPs
AAAI'06 proceedings of the 21st national conference on Artificial intelligence - Volume 2
Optimal and approximate Q-value functions for decentralized POMDPs
Journal of Artificial Intelligence Research
Memory-bounded dynamic programming for DEC-POMDPs
IJCAI'07 Proceedings of the 20th international joint conference on Artifical intelligence
Scaling up optimal heuristic search in Dec-POMDPs via incremental expansion
IJCAI'11 Proceedings of the Twenty-Second international joint conference on Artificial Intelligence - Volume Volume Three
Incremental clustering and expansion for faster optimal planning in decentralized POMDPs
Journal of Artificial Intelligence Research
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Bayesian games can be used to model single-shot decision problems in which agents only possess incomplete information about other agents, and hence are important for multiagent coordination under uncertainty. Moreover they can be used to represent different stages of sequential multiagent 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 solution 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 improvement over previous methods, obtaining speedups of up to 3 orders of magnitude for synthetic random games, and reaching 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 algorithms for decentralized POMDPs.