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

Author

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  • George E. Monahan

    (Georgia Institute of Technology)

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.

Suggested Citation

  • George E. Monahan, 1982. "State of the Art---A Survey of Partially Observable Markov Decision Processes: Theory, Models, and Algorithms," Management Science, INFORMS, vol. 28(1), pages 1-16, January.
  • Handle: RePEc:inm:ormnsc:v:28:y:1982:i:1:p:1-16
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    File URL: http://dx.doi.org/10.1287/mnsc.28.1.1
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    Cited by:

    1. Ricardo Montoya & Oded Netzer & Kamel Jedidi, 2010. "Dynamic Allocation of Pharmaceutical Detailing and Sampling for Long-Term Profitability," Marketing Science, INFORMS, vol. 29(5), pages 909-924, 09-10.
    2. James T. Treharne & Charles R. Sox, 2002. "Adaptive Inventory Control for Nonstationary Demand and Partial Information," Management Science, INFORMS, vol. 48(5), pages 607-624, May.
    3. Gong, Linguo & Tang, Kwei, 1997. "Monitoring machine operations using on-line sensors," European Journal of Operational Research, Elsevier, vol. 96(3), pages 479-492, February.
    4. Givon, Moshe & Grosfeld-Nir, Abraham, 2008. "Using partially observed Markov processes to select optimal termination time of TV shows," Omega, Elsevier, vol. 36(3), pages 477-485, June.
    5. Lian, Zhaotong & Deshmukh, Abhijit, 2006. "Performance prediction of an unmanned airborne vehicle multi-agent system," European Journal of Operational Research, Elsevier, vol. 172(2), pages 680-695, July.
    6. Serin, Yasemin, 1995. "A nonlinear programming model for partially observable Markov decision processes: Finite horizon case," European Journal of Operational Research, Elsevier, vol. 86(3), pages 549-564, November.
    7. Jingyu Zhang & Brian T. Denton & Hari Balasubramanian & Nilay D. Shah & Brant A. Inman, 2012. "Optimization of Prostate Biopsy Referral Decisions," Manufacturing & Service Operations Management, INFORMS, vol. 14(4), pages 529-547, October.
    8. Arifoglu, Kenan & Özekici, Süleyman, 2011. "Inventory management with random supply and imperfect information: A hidden Markov model," International Journal of Production Economics, Elsevier, vol. 134(1), pages 123-137, November.
    9. Kobayashi, Teruyoshi, 2009. "Announcements and the effectiveness of monetary policy: A view from the US prime rate," Journal of Banking & Finance, Elsevier, vol. 33(12), pages 2253-2266, December.
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    12. Chernonog, Tatyana & Avinadav, Tal, 2016. "A two-state partially observable Markov decision process with three actionsAuthor-Name: Ben-Zvi, Tal," European Journal of Operational Research, Elsevier, vol. 254(3), pages 957-967.
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    15. Baggio, Michele & Fackler, Paul L., 2016. "Optimal management with reversible regime shifts," Journal of Economic Behavior & Organization, Elsevier, vol. 132(PB), pages 124-136.
    16. repec:pal:jorsoc:v:57:y:2006:i:8:d:10.1057_palgrave.jors.2602048 is not listed on IDEAS
    17. Yossi Aviv & Amit Pazgal, 2005. "A Partially Observed Markov Decision Process for Dynamic Pricing," Management Science, INFORMS, vol. 51(9), pages 1400-1416, September.
    18. Arifoglu, Kenan & Özekici, Süleyman, 2010. "Optimal policies for inventory systems with finite capacity and partially observed Markov-modulated demand and supply processes," European Journal of Operational Research, Elsevier, vol. 204(3), pages 421-438, August.
    19. Fackler, Paul L. & Haight, Robert G., 2014. "Monitoring as a partially observable decision problem," Resource and Energy Economics, Elsevier, vol. 37(C), pages 226-241.
    20. White, Benedict, 2002. "Optimal Monitoring of Agri-environmental Schemes," 2002 Conference (46th), February 13-15, 2002, Canberra 125606, Australian Agricultural and Resource Economics Society.
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    22. Haight, Robert G. & Polasky, Stephen, 2010. "Optimal control of an invasive species with imperfect information about the level of infestation," Resource and Energy Economics, Elsevier, vol. 32(4), pages 519-533, November.
    23. Grosfeld-Nir, Abraham, 2007. "Control limits for two-state partially observable Markov decision processes," European Journal of Operational Research, Elsevier, vol. 182(1), pages 300-304, October.
    24. Jang, Wooseung & Shanthikumar, J. George, 2004. "Sequential process control under capacity constraints," European Journal of Operational Research, Elsevier, vol. 155(3), pages 695-714, June.

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