Computationally feasible bounds for partially observed Markov decision processes
Operations Research
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
Point-based value iteration: an anytime algorithm for POMDPs
IJCAI'03 Proceedings of the 18th international joint conference on Artificial intelligence
A heuristic variable grid solution method for POMDPs
AAAI'97/IAAI'97 Proceedings of the fourteenth national conference on artificial intelligence and ninth conference on Innovative applications of artificial intelligence
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
Real-Time decision making for large POMDPs
AI'05 Proceedings of the 18th Canadian Society conference on Advances in Artificial Intelligence
Belief selection in point-based planning algorithms for POMDPs
AI'06 Proceedings of the 19th international conference on Advances in Artificial Intelligence: Canadian Society for Computational Studies of Intelligence
Scaling up: solving POMDPs through value based clustering
AAAI'07 Proceedings of the 22nd national conference on Artificial intelligence - Volume 2
Scaling up: solving POMDPs through value based clustering
AAAI'07 Proceedings of the 22nd national conference on Artificial intelligence - Volume 2
Canadian traveler problem with remote sensing
IJCAI'09 Proceedings of the 21st international jont conference on Artifical intelligence
Topological order planner for POMDPs
IJCAI'09 Proceedings of the 21st international jont conference on Artifical intelligence
Evaluating point-based POMDP solvers on multicore machines
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics - Special issue on gait analysis
Scheduling sensors for monitoring sentient spaces using an approximate POMDP policy
Pervasive and Mobile Computing
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Recent scaling up of POMDP solvers towards realistic applications is largely due to point-based methods such as PBVI, Perseus, and HSVI, which quickly converge to an approximate solution for medium-sized problems. These algorithms improve a value function by using backup operations over a single belief point. In the simpler domain of MDP solvers, prioritizing the order of equivalent backup operations on states is well known to speed up convergence. We generalize the notion of prioritized backups to the POMDP framework, and show that the ordering of backup operations on belief points is important. We also present a new algorithm, Prioritized Value Iteration (PVI), and show empirically that it outperforms current point-based algorithms. Finally, a new empirical evaluation measure, based on the number of backups and the number of belief points, is proposed, in order to provide more accurate benchmark comparisons.