Integrating POMDP and reinforcement learning for a two layer simulated robot architecture
Proceedings of the third annual conference on Autonomous Agents
Equivalence notions and model minimization in Markov decision processes
Artificial Intelligence - special issue on planning with uncertainty and incomplete information
Region-based incremental pruning for POMDPs
UAI '04 Proceedings of the 20th conference on Uncertainty in artificial intelligence
A Navigation System for Assistant Robots Using Visually Augmented POMDPs
Autonomous Robots
Efficient maximization in solving POMDPs
AAAI'05 Proceedings of the 20th national conference on Artificial intelligence - Volume 2
A model approximation scheme for planning in partially observable stochastic domains
Journal of Artificial Intelligence Research
Approximating optimal policies for partially observable stochastic domains
IJCAI'95 Proceedings of the 14th international joint conference on Artificial intelligence - Volume 2
Distributed spectrum sensing and access in cognitive radio networks with energy constraint
IEEE Transactions on Signal Processing
A cooperative retransmission scheme in wireless networks with imperfect channel state information
WCNC'09 Proceedings of the 2009 IEEE conference on Wireless Communications & Networking Conference
POMDP filter: pruning POMDP value functions with the Kaczmarz iterative method
MICAI'10 Proceedings of the 9th Mexican international conference on Advances in artificial intelligence: Part I
Moral minds as multiple-layer organizations
IBERAMIA'10 Proceedings of the 12th Ibero-American conference on Advances in artificial intelligence
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Markov decision processes (MDP''s) are a mathematical formalization of problems in which a decision-maker must choose how to act to maximize its reward over a series of interactions with its environment. Partially observable Markov decision processes (POMDP''s) generalize the MDP framework to the case where the agent must make its decisions in partial ignorance of its current situation. This paper describes the POMDP framework and presents some well-known results from the field. It then presents a novel method called the witness algorithm for solving POMDP problems and analyzes its computational complexity. The paper argues that the witness algorithm is superior to existing algorithms for solving POMDP''s in an important complexity-theoretic sense.