Data networks
The complexity of Markov decision processes
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Computationally feasible bounds for partially observed Markov decision processes
Operations Research
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Artificial Intelligence - Special volume on computational research on interaction and agency, part 1
On the complexity of partially observed Markov decision processes
Theoretical Computer Science - Special issue on complexity theory and the theory of algorithms as developed in the CIS
Planning and acting in partially observable stochastic domains
Artificial Intelligence
Heuristic search in cyclic AND/OR graphs
AAAI '98/IAAI '98 Proceedings of the fifteenth national/tenth conference on Artificial intelligence/Innovative applications of artificial intelligence
Markov Decision Processes: Discrete Stochastic Dynamic Programming
Markov Decision Processes: Discrete Stochastic Dynamic Programming
Policy Iteration for Factored MDPs
UAI '00 Proceedings of the 16th Conference on Uncertainty in Artificial Intelligence
BI-POMDP: Bounded, Incremental, Partially-Observable Markov-Model Planning
ECP '97 Proceedings of the 4th European Conference on Planning: Recent Advances in AI Planning
Dynamic Programming
Efficient dynamic-programming updates in partially observable Markov decision processes
Efficient dynamic-programming updates in partially observable Markov decision processes
Exact and approximate algorithms for partially observable markov decision processes
Exact and approximate algorithms for partially observable markov decision processes
Finite-memory control of partially observable systems
Finite-memory control of partially observable systems
Algorithms for partially observable markov decision processes
Algorithms for partially observable markov decision processes
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
A model approximation scheme for planning in partially observable stochastic domains
Journal of Artificial Intelligence Research
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Journal of Artificial Intelligence Research
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IJCAI'03 Proceedings of the 18th international joint conference on Artificial intelligence
An improved grid-based approximation algorithm for POMDPs
IJCAI'01 Proceedings of the 17th international joint conference on Artificial intelligence - Volume 1
Approximating optimal policies for partially observable stochastic domains
IJCAI'95 Proceedings of the 14th international joint conference on Artificial intelligence - Volume 2
Decomposition techniques for planning in stochastic domains
IJCAI'95 Proceedings of the 14th international joint conference on Artificial intelligence - Volume 2
A POMDP approximation algorithm that anticipates the need to observe
PRICAI'00 Proceedings of the 6th Pacific Rim international conference on Artificial intelligence
Computing optimal policies for partially observable decision processes using compact representations
AAAI'96 Proceedings of the thirteenth national conference on Artificial intelligence - Volume 2
Incremental methods for computing bounds in partially observable 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
Flexible decomposition algorithms for weakly coupled Markov decision problems
UAI'98 Proceedings of the Fourteenth conference on Uncertainty in 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
Partially observable Markov decision processes with imprecise parameters
Artificial Intelligence
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Value iteration is a popular algorithm for finding near optimal policies for POMDPs. It is ineffcient due to the need to account for the entire belief space, which necessitates the solution of large numbers of linear programs. In this paper, we study value iteration restricted to belief subsets. We show that, together with properly chosen belief subsets, restricted value iteration yields near-optimal policies and we give a condition for determining whether a given belief subset would bring about savings in space and time. We also apply restricted value iteration to two interesting classes of POMDPs, namely informative POMDPs and near-discernible POMDPs.