Markov Decision Processes: Discrete Stochastic Dynamic Programming
Markov Decision Processes: Discrete Stochastic Dynamic Programming
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
Region-based incremental pruning for POMDPs
UAI '04 Proceedings of the 20th conference on Uncertainty in artificial intelligence
Exploiting structure to efficiently solve large scale partially observable markov decision processes
Exploiting structure to efficiently solve large scale partially observable markov decision processes
Control Techniques for Complex Networks
Control Techniques for Complex Networks
Indefinite-horizon POMDPs with action-based termination
AAAI'07 Proceedings of the 22nd national conference on Artificial intelligence - Volume 2
Perseus: randomized point-based value iteration for POMDPs
Journal of Artificial Intelligence Research
A Decision-Theoretic Framework for Opportunistic Spectrum Access
IEEE Wireless Communications
Constrained Markovian decision processes: the dynamic programming approach
Operations Research Letters
Quickest detection in multiple on-off processes
IEEE Transactions on Signal Processing
Point-based value iteration for constrained POMDPs
IJCAI'11 Proceedings of the Twenty-Second international joint conference on Artificial Intelligence - Volume Volume Three
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We describe an exact dynamic programming update for constrained partially observable Markov decision processes (CPOMDPs). State-of-the-art exact solution of unconstrained POMDPs relies on implicit enumeration of the vectors in the piecewise linear value function, and pruning operations to obtain a minimal representation of the updated value function. In dynamic programming for CPOMDPs, each vector takes two valuations, one with respect to the objective function and another with respect to the constraint function. The dynamic programming update consists of finding, for each belief state, the vector that has the best objective function valuation while still satisfying the constraint function. Whereas the pruning operation in an unconstrained POMDP requires solution of a linear program, the pruning operation for CPOMDPs requires solution of a mixed integer linear program.