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State of the Art---A Survey of Partially Observable Markov Decision Processes: Theory, Models, and Algorithms

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  • George E. Monahan

    (Georgia Institute of Technology)

Abstract

This paper surveys models and algorithms dealing with partially observable Markov decision processes. A partially observable Markov decision process (POMDP) is a generalization of a Markov decision process which permits uncertainty regarding the state of a Markov process and allows for state information acquisition. A general framework for finite state and action POMDP's is presented. Next, there is a brief discussion of the development of POMDP's and their relationship with other decision processes. A wide range of models in such areas as quality control, machine maintenance, internal auditing, learning, and optimal stopping are discussed within the POMDP-framework. Lastly, algorithms for computing optimal solutions to POMDP's are presented.

Suggested Citation

  • George E. Monahan, 1982. "State of the Art---A Survey of Partially Observable Markov Decision Processes: Theory, Models, and Algorithms," Management Science, INFORMS, vol. 28(1), pages 1-16, January.
  • Handle: RePEc:inm:ormnsc:v:28:y:1982:i:1:p:1-16
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    File URL: http://dx.doi.org/10.1287/mnsc.28.1.1
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