State of the Art—A Survey of Partially Observable Markov Decision Processes: Theory, Models, and Algorithms
综述了部分可观测马尔可夫决策过程（POMDP）的模型与算法，介绍了其与马尔可夫决策过程的关系，并讨论了在质量控制、机器维护、审计、学习和最优停止等领域的应用。
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.