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Market-Based Reinforcement Learning in Partially Observable Worlds
ICANN '01 Proceedings of the International Conference on Artificial Neural Networks
Sequential Decision Making Based on Direct Search
Sequence Learning - Paradigms, Algorithms, and Applications
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ICDM '03 Proceedings of the Third IEEE International Conference on Data Mining
An online POMDP algorithm for complex multiagent environments
Proceedings of the fourth international joint conference on Autonomous agents and multiagent systems
Probabilistic incremental program evolution
Evolutionary Computation
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IAAI'05 Proceedings of the 17th conference on Innovative applications of artificial intelligence - Volume 3
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IJCAI'97 Proceedings of the Fifteenth international joint conference on Artifical intelligence - Volume 2
Motion in ambiguity: Coordinated active global localization for multiple robots
Robotics and Autonomous Systems
Managing Adaptive Versatile environments
Pervasive and Mobile Computing
A probabilistic particle-control approximation of chance-constrained stochastic predictive control
IEEE Transactions on Robotics
Solving POMDPs by searching in policy space
UAI'98 Proceedings of the Fourteenth conference on Uncertainty in artificial intelligence
The complexity of plan existence and evaluation in robabilistic domains
UAI'97 Proceedings of the Thirteenth conference on Uncertainty in artificial intelligence
UAI'96 Proceedings of the Twelfth international conference on Uncertainty in artificial intelligence
An applied optimization framework for distributed air transportation environments
DEXA'06 Proceedings of the 17th international conference on Database and Expert Systems Applications
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In this paper, we bring techniques from operations research to bear on the problem of choosing optimal actions in partially observable stochastic domains. We begin by introducing the theory of Markov decision processes (MDPs) and partially observable MDPs (POMDPs). We then outline a novel algorithm for solving POMDPs off line and show how, in some cases, a finite-memory controller can be extracted from the solution to a POMDP. We conclude with a discussion of the complexity of finding exact solutions to POMDPs and of some possibilities for finding approximate solutions.