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A Survey of Partially Observable Markov Decision Processes: Theory, Models, and Algorithms

George E. Monahan
Management Science
Vol. 28, No. 1 (Jan., 1982), pp. 1-16
Published by: INFORMS
Stable URL: http://www.jstor.org/stable/2631070
Page Count: 16
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A Survey of Partially Observable Markov Decision Processes: Theory, Models, and Algorithms
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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.

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