A POMDP Approximation Algorithm That Anticipates the Need to Observe
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[1] Andrew McCallum,et al. Instance-Based Utile Distinctions for Reinforcement Learning with Hidden State , 1995, ICML.
[2] Daphne Koller,et al. Reinforcement Learning Using Approximate Belief States , 1999, NIPS.
[3] Blai Bonet,et al. Planning with Incomplete Information as Heuristic Search in Belief Space , 2000, AIPS.
[4] Leslie Pack Kaelbling,et al. Learning Policies for Partially Observable Environments: Scaling Up , 1997, ICML.
[5] Stuart J. Russell,et al. Approximating Optimal Policies for Partially Observable Stochastic Domains , 1995, IJCAI.
[6] Leslie Pack Kaelbling,et al. Acting under uncertainty: discrete Bayesian models for mobile-robot navigation , 1996, Proceedings of IEEE/RSJ International Conference on Intelligent Robots and Systems. IROS '96.
[7] Eric A. Hansen,et al. Cost-Effective Sensing during Plan Execution , 1994, AAAI.
[8] Anne Condon,et al. On the Undecidability of Probabilistic Planning and Infinite-Horizon Partially Observable Markov Decision Problems , 1999, AAAI/IAAI.
[9] Eric A. Hansen,et al. Solving POMDPs by Searching in Policy Space , 1998, UAI.
[10] John N. Tsitsiklis,et al. Neuro-Dynamic Programming , 1996, Encyclopedia of Machine Learning.
[11] Ronald A. Howard,et al. Information Value Theory , 1966, IEEE Trans. Syst. Sci. Cybern..
[12] John N. Tsitsiklis,et al. The Complexity of Markov Decision Processes , 1987, Math. Oper. Res..
[13] Michael L. Littman,et al. Incremental Pruning: A Simple, Fast, Exact Method for Partially Observable Markov Decision Processes , 1997, UAI.