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

George E. Monahan
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George E. Monahan: Georgia Institute of Technology

Management Science, 1982, vol. 28, issue 1, 1-16

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.

Keywords: Markov decision processes; partially observable; survey (search for similar items in EconPapers)
Date: 1982
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