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

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.

• 저널Management Science · 28(1) · 1–16
• 토픽Fault Detection and Control Systems · Control and Systems Engineering
• DOI10.1287/mnsc.28.1.1
• 저자George E. Monahan