Planning and acting in partially observable stochastic domains
Artificial Intelligence
Bounded-parameter Markov decision process
Artificial Intelligence
Computers and Intractability: A Guide to the Theory of NP-Completeness
Computers and Intractability: A Guide to the Theory of NP-Completeness
The Complexity of Decentralized Control of Markov Decision Processes
Mathematics of Operations Research
Symmetries and Model Minimization in Markov Decision Processes
Symmetries and Model Minimization in Markov Decision Processes
Graph Theory With Applications
Graph Theory With Applications
Anytime point-based approximations for large POMDPs
Journal of Artificial Intelligence Research
Computing optimal policies for partially observable decision processes using compact representations
AAAI'96 Proceedings of the thirteenth national conference on Artificial intelligence - Volume 2
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
Policy-contingent abstraction for robust robot control
UAI'03 Proceedings of the Nineteenth conference on Uncertainty in Artificial Intelligence
Identifying and exploiting weak-information inducing actions in solving POMDPs
The 10th International Conference on Autonomous Agents and Multiagent Systems - Volume 3
Exploiting symmetries for single- and multi-agent Partially Observable Stochastic Domains
Artificial Intelligence
Proceedings of the twelfth workshop on Foundations of genetic algorithms XII
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We extend the model minimization technique for partially observable Markov decision processes (POMDPs) to handle symmetries in the joint space of states, actions, and observations. The POMDP symmetry we define in this paper cannot be handled by the model minimization techniques previously published in the literature. We formulate the problem of finding the symmetries as a graph automorphism (GA) problem, and although not yet known to be tractable, we experimentally show that the sparseness of the graph representing the POMDP allows us to quickly find symmetries. We show how the symmetries in POMDPs can be exploited for speeding up point-based algorithms. We experimentally demonstrate the effectiveness of our approach.