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- Title
A SURVEY OF PARTIALLY OBSERVABLE MARKOV DECISION PROCESSES: THEORY, MODELS, AND ALGORITHMS.
- Authors
Monahan, George E.
- 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.
- Subjects
ALGORITHMS; MARKOV processes; STOCHASTIC processes; DECISION making; MATHEMATICAL models; FACILITY management; STATISTICAL process control; UNCERTAINTY; INTERNAL auditing; QUALITY control; OPTIMAL stopping (Mathematical statistics); EDUCATION
- Publication
Management Science, 1982, Vol 28, Issue 1, p1
- ISSN
0025-1909
- Publication type
Article
- DOI
10.1287/mnsc.28.1.1