Artificial Intelligence
Learning to act using real-time dynamic programming
Artificial Intelligence - Special volume on computational research on interaction and agency, part 1
Planning and acting in partially observable stochastic domains
Artificial Intelligence
Dynamic Programming and Optimal Control, Two Volume Set
Dynamic Programming and Optimal Control, Two Volume Set
Introduction to Reinforcement Learning
Introduction to Reinforcement Learning
Neuro-Dynamic Programming
Learning Sorting and Decision Trees with POMDPs
ICML '98 Proceedings of the Fifteenth International Conference on Machine Learning
An epsilon-Optimal Grid-Based Algorithm for Partially Observable Markov Decision Processes
ICML '02 Proceedings of the Nineteenth International Conference on Machine Learning
Value-function approximations for partially observable Markov decision processes
Journal of Artificial Intelligence Research
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
Forward search value iteration for POMDPs
IJCAI'07 Proceedings of the 20th international joint conference on Artifical intelligence
Efficient planning under uncertainty with macro-actions
Journal of Artificial Intelligence Research
Adaptive decision support for structured organizations: a case for OrgPOMDPs
The 10th International Conference on Autonomous Agents and Multiagent Systems - Volume 3
Decision Support in Organizations: A Case for OrgPOMDPs
WI-IAT '11 Proceedings of the 2011 IEEE/WIC/ACM International Conferences on Web Intelligence and Intelligent Agent Technology - Volume 02
DetH: approximate hierarchical solution of large Markov decision processes
IJCAI'11 Proceedings of the Twenty-Second international joint conference on Artificial Intelligence - Volume Volume Three
Goal recognition over POMDPs: inferring the intention of a POMDP agent
IJCAI'11 Proceedings of the Twenty-Second international joint conference on Artificial Intelligence - Volume Volume Three
A survey of point-based POMDP solvers
Autonomous Agents and Multi-Agent Systems
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Point-based algorithms and RTDP-Bel are approximate methods for solving POMDPs that replace the full updates of parallel value iteration by faster and more effective updates at selected beliefs. An important difference between the two methods is that the former adopt Sondik's representation of the value function, while the latter uses a tabular representation and a discretization function. The algorithms, however, have not been compared up to now, because they target different POMDPs: discounted POMDPs on the one hand, and Goal POMDPs on the other. In this paper, we bridge this representational gap, showing how to transform discounted POMDPs into Goal POMDPs, and use the transformation to compare RTDP-Bel with point-based algorithms over the existing discounted benchmarks. The results appear to contradict the conventional wisdom in the area showing that RTDP-Bel is competitive, and sometimes superior to point-based algorithms in both quality and time.