You are not currently logged in.
Access JSTOR through your library or other institution:
A Survey of Partially Observable Markov Decision Processes: Theory, Models, and Algorithms
George E. Monahan
Vol. 28, No. 1 (Jan., 1982), pp. 1-16
Published by: INFORMS
Stable URL: http://www.jstor.org/stable/2631070
Page Count: 16
You can always find the topics here!Topics: Markov processes, Optimal policy, Algorithms, Mathematical problems, Markov chains, Quality assurance, Unobservables, Markov models, Cost efficiency
Were these topics helpful?See somethings inaccurate? Let us know!
Select the topics that are inaccurate.
Preview not available
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.
Management Science © 1982 INFORMS