Heuristic search value iteration for POMDPs
UAI '04 Proceedings of the 20th conference on Uncertainty in artificial intelligence
Value-function approximations for partially observable Markov decision processes
Journal of Artificial Intelligence Research
Speeding up the convergence of value iteration in partially observable Markov decision processes
Journal of Artificial Intelligence 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
Using core beliefs for point-based value iteration
IJCAI'05 Proceedings of the 19th international joint conference on 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
An Uncertainty-Based Belief Selection Method for POMDP Value Iteration
ECSQARU '09 Proceedings of the 10th European Conference on Symbolic and Quantitative Approaches to Reasoning with Uncertainty
Optimizing anthrax outbreak detection using reinforcement learning
IAAI'07 Proceedings of the 19th national conference on Innovative applications of artificial intelligence - Volume 2
Prioritizing point-based POMDP solvers
ECML'06 Proceedings of the 17th European conference on Machine Learning
A survey of point-based POMDP solvers
Autonomous Agents and Multi-Agent Systems
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Current point-based planning algorithms for solving partially observable Markov decision processes (POMDPs) have demonstrated that a good approximation of the value function can be derived by interpolation from the values of a specially selected set of points. The performance of these algorithms can be improved by eliminating unnecessary backups or concentrating on more important points in the belief simplex. We study three methods designed to improve point-based value iteration algorithms. The first two methods are based on reachability analysis on the POMDP belief space. This approach relies on prioritizing the beliefs based on how they are reached from the given initial belief state. The third approach is motivated by the observation that beliefs which are the most overestimated or underestimated have greater influence on the precision of value function than other beliefs. We present an empirical evaluation illustrating how the performance of point-based value iteration (Pineau et al., 2003) varies with these approaches.