Exploiting the architecture of dynamic systems
AAAI '99/IAAI '99 Proceedings of the sixteenth national conference on Artificial intelligence and the eleventh Innovative applications of artificial intelligence conference innovative applications of artificial intelligence
Dynamic bayesian networks: representation, inference and learning
Dynamic bayesian networks: representation, inference and learning
Exploiting locality of interaction in factored Dec-POMDPs
Proceedings of the 7th international joint conference on Autonomous agents and multiagent systems - Volume 1
Bounded policy iteration for decentralized POMDPs
IJCAI'05 Proceedings of the 19th international joint conference on Artificial intelligence
Point-based backup for decentralized POMDPs: complexity and new algorithms
Proceedings of the 9th International Conference on Autonomous Agents and Multiagent Systems: volume 1 - Volume 1
Efficient planning in large POMDPs through policy graph based factorized approximations
ECML PKDD'10 Proceedings of the 2010 European conference on Machine learning and knowledge discovery in databases: Part III
Tractable inference for complex stochastic processes
UAI'98 Proceedings of the Fourteenth conference on Uncertainty in artificial intelligence
An optimal best-first search algorithm for solving infinite horizon DEC-POMDPs
ECML'05 Proceedings of the 16th European conference on Machine Learning
Heuristic search of multiagent influence space
Proceedings of the 11th International Conference on Autonomous Agents and Multiagent Systems - Volume 2
Producing efficient error-bounded solutions for transition independent decentralized mdps
Proceedings of the 2013 international conference on Autonomous agents and multi-agent systems
Approximate solutions for factored Dec-POMDPs with many agents
Proceedings of the 2013 international conference on Autonomous agents and multi-agent systems
Incremental clustering and expansion for faster optimal planning in decentralized POMDPs
Journal of Artificial Intelligence Research
Monte-Carlo expectation maximization for decentralized POMDPs
IJCAI'13 Proceedings of the Twenty-Third international joint conference on Artificial Intelligence
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Decentralized partially observable Markov decision processes (DEC-POMDPs) are used to plan policies for multiple agents that must maximize a joint reward function but do not communicate with each other. The agents act under uncertainty about each other and the environment. This planning task arises in optimization of wireless networks, and other scenarios where communication between agents is restricted by costs or physical limits. DEC-POMDPs are a promising solution, but optimizing policies quickly becomes computationally intractable when problem size grows. Factored DEC-POMDPs allow large problems to be described in compact form, but have the same worst case complexity as non-factored DEC-POMDPs. We propose an efficient optimization algorithm for large factored infinite-horizon DEC-POMDPs. We formulate expectation-maximization based optimization into a new form, where complexity can be kept tractable by factored approximations. Our method performs well, and it can solve problems with more agents and larger state spaces than state of the art DEC-POMDP methods. We give results for factored infinite-horizon DEC-POMDP problems with up to 10 agents.