Computationally feasible bounds for partially observed Markov decision processes
Operations Research
Acting optimally in partially observable stochastic domains
AAAI'94 Proceedings of the twelfth national conference on Artificial intelligence (vol. 2)
Efficient dynamic-programming updates in partially observable Markov decision processes
Efficient dynamic-programming updates in partially observable Markov decision processes
Heuristic search value iteration for POMDPs
UAI '04 Proceedings of the 20th conference on Uncertainty in artificial intelligence
Prioritization Methods for Accelerating MDP Solvers
The Journal of Machine Learning Research
Perseus: randomized point-based value iteration for POMDPs
Journal of Artificial Intelligence Research
An improved grid-based approximation algorithm for POMDPs
IJCAI'01 Proceedings of the 17th international joint conference on Artificial intelligence - Volume 1
Incremental pruning: a simple, fast, exact method for partially observable Markov decision processes
UAI'97 Proceedings of the Thirteenth conference on Uncertainty in artificial intelligence
Prioritizing point-based POMDP solvers
ECML'06 Proceedings of the 17th European conference on Machine Learning
Real-Time decision making for large POMDPs
AI'05 Proceedings of the 18th Canadian Society conference on Advances in Artificial Intelligence
Topological order planner for POMDPs
IJCAI'09 Proceedings of the 21st international jont conference on Artifical intelligence
Improving POMDP tractability via belief compression and clustering
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
A survey of point-based POMDP solvers
Autonomous Agents and Multi-Agent Systems
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Partially Observable Markov Decision Processes (POMDPs) provide an appropriately rich model for agents operating under partial knowledge of the environment. Since finding an optimal POMDP policy is intractable, approximation techniques have been a main focus of research, among them point-based algorithms, which scale up relatively well - up to thousands of states. An important decision in a point-based algorithm is the order of backup operations over belief states. Prioritization techniques for ordering the sequence of backup operations reduce the number of needed backups considerably, but involve significant overhead. This paper suggests a new way to order backups, based on a soft clustering of the belief space. Our novel soft clustering method relies on the solution of the underlying MDP. Empirical evaluation verifies that our method rapidly computes a good order of backups, showing orders of magnitude improvement in runtime over a number of benchmarks.