The complexity of Markov decision processes
Mathematics of Operations Research
A Survey of solution techniques for the partially observed Markov decision process
Annals of Operations Research
The Complexity of Decentralized Control of Markov Decision Processes
Mathematics of Operations Research
On the undecidability of probabilistic planning and related stochastic optimization problems
Artificial Intelligence - special issue on planning with uncertainty and incomplete information
Equivalence notions and model minimization in Markov decision processes
Artificial Intelligence - special issue on planning with uncertainty and incomplete information
Symmetries and Model Minimization in Markov Decision Processes
Symmetries and Model Minimization in Markov Decision Processes
Heuristic search value iteration for POMDPs
UAI '04 Proceedings of the 20th conference on Uncertainty in artificial intelligence
Algorithmic Game Theory
Q-value functions for decentralized POMDPs
Proceedings of the 6th international joint conference on Autonomous agents and multiagent systems
On the hardness of finding symmetries in Markov decision processes
Proceedings of the 25th international conference on Machine learning
The permutable POMDP: fast solutions to POMDPs for preference elicitation
Proceedings of the 7th international joint 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
Exploiting symmetries in POMDPs for point-based algorithms
AAAI'08 Proceedings of the 23rd national conference on Artificial intelligence - Volume 2
Perseus: randomized point-based value iteration for POMDPs
Journal of Artificial Intelligence Research
Anytime point-based approximations for large POMDPs
Journal of Artificial Intelligence Research
Policy iteration for decentralized control of Markov decision processes
Journal of Artificial Intelligence Research
Memory-bounded dynamic programming for DEC-POMDPs
IJCAI'07 Proceedings of the 20th international joint conference on Artifical intelligence
Forward search value iteration for 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
Bounded policy iteration for decentralized POMDPs
IJCAI'05 Proceedings of the 19th international joint conference on Artificial intelligence
Planning and acting in partially observable stochastic domains
Artificial Intelligence
Point-based policy generation for decentralized POMDPs
Proceedings of the 9th International Conference on Autonomous Agents and Multiagent Systems: volume 1 - Volume 1
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
Model minimization in Markov decision processes
AAAI'97/IAAI'97 Proceedings of the fourteenth national conference on artificial intelligence and ninth conference on Innovative applications of artificial intelligence
Point-based bounded policy iteration for decentralized POMDPs
PRICAI'10 Proceedings of the 11th Pacific Rim international conference on Trends in artificial intelligence
Policy-contingent abstraction for robust robot control
UAI'03 Proceedings of the Nineteenth conference on Uncertainty in Artificial Intelligence
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While Partially Observable Markov Decision Processes (POMDPs) and their multi-agent extension Partially Observable Stochastic Games (POSGs) provide a natural and systematic approach to modeling sequential decision making problems under uncertainty, the computational complexity with which the solutions are computed is known to be prohibitively expensive. In this paper, we show how such high computational resource requirements can be alleviated through the use of symmetries present in the problem. The problem of finding the symmetries can be cast as a graph automorphism (GA) problem on a graphical representation of the problem. We demonstrate how such symmetries can be exploited in order to speed up the solution computation and provide computational complexity results.