The Complexity of Decentralized Control of Markov Decision Processes
Mathematics of Operations Research
Not all agents are equal: scaling up distributed POMDPs for agent networks
Proceedings of the 7th international joint conference on Autonomous agents and multiagent systems - Volume 1
Value-based observation compression for DEC-POMDPs
Proceedings of the 7th international joint conference on Autonomous agents and multiagent systems - Volume 1
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
Networked distributed POMDPs: a synthesis of distributed constraint optimization and POMDPs
AAAI'05 Proceedings of the 20th national conference on Artificial intelligence - Volume 1
Solving transition independent decentralized Markov decision processes
Journal of Artificial Intelligence Research
Taming decentralized POMDPs: towards efficient policy computation for multiagent settings
IJCAI'03 Proceedings of the 18th international joint conference on Artificial intelligence
Event-detecting multi-agent MDPs: complexity and constant-factor approximation
IJCAI'09 Proceedings of the 21st international jont conference on Artifical 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
Distributed planning in hierarchical factored MDPs
UAI'02 Proceedings of the Eighteenth conference on Uncertainty in artificial intelligence
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Decentralized Partially Observable Markov Decision Problem, DEC-POMDP is a popular model to representmulti-agent decision making under uncertainty. However, the significant computational complexity involved in solving DECPOMDPshas limited their application. Recently, social model shaping (TREMOR and D-TREMOR algorithms) was introduced as an alternative to solve a sub-class of DEC-POMDPs. While scalability has been improved to even solve hundred agent problems, social model shaping has been restricted to solving a sub-class of DEC-POMDPs called Distributed POMDPs with Coordination Locales (DPCL). To that end, we make two significant contributions: (a) Firstly, we enhance the model shaping approach to solve general DEC-POMDPs where there is no restriction on the agent dependencies, and (b) Secondly, we provide theoretical justification for the model shaping heuristics. The key intuition is that not all interactions between agents occur at every time step. In addition to solving 100 agent problems in weakly coupled domains (due to extension from TREMOR and D-TREMOR), we are able to show that social model shaping achieves comparable performance to leading DEC-POMDP solvers (such as IMBDP, MBDP-OC, PBIP-IPGetc.) on tightly coupled benchmark problems.