Computationally feasible bounds for partially observed Markov decision processes
Operations Research
Exact and approximate algorithms for partially observable markov decision processes
Exact and approximate algorithms for partially observable markov decision processes
Algorithms for partially observable markov decision processes
Algorithms for partially observable markov decision processes
A model approximation scheme for planning in partially observable stochastic domains
Journal of Artificial Intelligence Research
Decomposition techniques for planning in stochastic domains
IJCAI'95 Proceedings of the 14th international joint conference on Artificial intelligence - Volume 2
Model minimization in Markov decision processes
AAAI'97/IAAI'97 Proceedings of the fourteenth national conference on artificial intelligence and ninth conference on Innovative applications of artificial intelligence
Structured reachability analysis for Markov decision processes
UAI'98 Proceedings of the Fourteenth conference on Uncertainty in artificial intelligence
Efficient maximization in solving POMDPs
AAAI'05 Proceedings of the 20th national conference on Artificial intelligence - Volume 2
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Value iteration is a popular algorithm for solving POMDPs. However, it is inefficient in practice. The primary reason is that it needs to conduct value updates for all the belief states in the (continuous) belief space. In this paper, we study value iteration working with a subset of the belief space, i.e., it conducts value updates only for belief states in the subset. We present a way to select belief subset and describe an algorithm to conduct value iteration over the selected subset. The algorithm is attractive in that it works with belief subset but also retains the quality of the generated values. Given a POMDP, we show how to a priori determine whether the selected subset is a proper subset of belief space. If this is the case, the algorithm carries the advantages of representation in space and efficiency in time.