The Complexity of Decentralized Control of Markov Decision Processes
Mathematics of Operations Research
Artificial Intelligence: A Modern Approach
Artificial Intelligence: A Modern Approach
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
Decentralized Markov Decision Processes with Event-Driven Interactions
AAMAS '04 Proceedings of the Third International Joint Conference on Autonomous Agents and Multiagent Systems - Volume 1
Letting loose a SPIDER on a network of POMDPs: generating quality guaranteed policies
Proceedings of the 6th international joint conference on Autonomous agents and multiagent systems
Exploiting locality of interaction in factored Dec-POMDPs
Proceedings of the 7th international joint conference on Autonomous agents and multiagent systems - Volume 1
Networked distributed POMDPs: a synthesis of distributed constraint optimization and POMDPs
AAAI'05 Proceedings of the 20th national conference on Artificial intelligence - Volume 1
Value-function approximations for partially observable Markov decision processes
Journal of Artificial Intelligence Research
Solving transition independent decentralized Markov decision processes
Journal of Artificial Intelligence Research
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
Taming decentralized POMDPs: towards efficient policy computation for multiagent settings
IJCAI'03 Proceedings of the 18th international joint conference on Artificial intelligence
Planning and acting in partially observable stochastic domains
Artificial Intelligence
Distributed model shaping for scaling to decentralized POMDPs with hundreds of agents
The 10th International Conference on Autonomous Agents and Multiagent Systems - Volume 3
Abstracting influences for efficient multiagent coordination under uncertainty
Abstracting influences for efficient multiagent coordination under uncertainty
Efficient planning for factored infinite-horizon DEC-POMDPs
IJCAI'11 Proceedings of the Twenty-Second international joint conference on Artificial Intelligence - Volume Volume One
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
Scalable multiagent planning using probabilistic inference
IJCAI'11 Proceedings of the Twenty-Second international joint conference on Artificial Intelligence - Volume Volume Three
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Multiagent planning under uncertainty has seen important progress in recent years. Two techniques, in particular, have substantially advanced efficiency and scalability of planning. Multiagent heuristic search gains traction by pruning large portions of the joint policy space deemed suboptimal by heuristic bounds. Alternatively, influence-based abstraction reformulates the search space of joint policies into a smaller space of influences, which represent the probabilistic effects that agents' policies may exert on one another. These techniques have been used independently, but never together, to solve larger problems (for Dec-POMDPs and subclasses) than previously possible. In this paper, we take the logical albeit nontrivial next step of combining multiagent A* search and influence-based abstraction into a single algorithm. The mathematical foundation that we provide, such as partially-specified influence evaluation and admissible heuristic definition, enables an investigation into whether the two techniques bring complementary gains. Our empirical results indicate that A* can provide significant computational savings on top of those already afforded by influence-space search, thereby bringing a significant contribution to the field of multiagent planning under uncertainty.