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Markovian Decision Processes with Probabilistic Observation of States

Author

Listed:
  • J. K. Satia

    (Northeastern University)

  • R. E. Lave

    (Stanford University)

Abstract

This is a study of finite state discrete time discounted Markovian decision process when the states are probabilistically observed. A model of this process is formulated, and an implicit enumeration algorithm is presented which optimizes the total expected discounted reward given the initial state. Several numerical examples are presented.

Suggested Citation

  • J. K. Satia & R. E. Lave, 1973. "Markovian Decision Processes with Probabilistic Observation of States," Management Science, INFORMS, vol. 20(1), pages 1-13, September.
    • Handle: RePEc:inm:ormnsc:v:20:y:1973:i:1:p:1-13
    DOI: 10.1287/mnsc.20.1.1
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    Cited by:

    1. Zeynep Turgay & Fikri Karaesmen & Egemen Lerzan Örmeci, 2018. "Structural properties of a class of robust inventory and queueing control problems," Naval Research Logistics (NRL), John Wiley & Sons, vol. 65(8), pages 699-716, December.
    2. Arnab Nilim & Laurent El Ghaoui, 2005. "Robust Control of Markov Decision Processes with Uncertain Transition Matrices," Operations Research, INFORMS, vol. 53(5), pages 780-798, October.
    3. Peter Buchholz & Dimitri Scheftelowitsch, 2019. "Computation of weighted sums of rewards for concurrent MDPs," Mathematical Methods of Operations Research, Springer;Gesellschaft für Operations Research (GOR);Nederlands Genootschap voor Besliskunde (NGB), vol. 89(1), pages 1-42, February.
    4. V Varagapriya & Vikas Vikram Singh & Abdel Lisser, 2023. "Joint chance-constrained Markov decision processes," Annals of Operations Research, Springer, vol. 322(2), pages 1013-1035, March.
    5. Andrew J. Keith & Darryl K. Ahner, 2021. "A survey of decision making and optimization under uncertainty," Annals of Operations Research, Springer, vol. 300(2), pages 319-353, May.
    6. Erick Delage & Shie Mannor, 2010. "Percentile Optimization for Markov Decision Processes with Parameter Uncertainty," Operations Research, INFORMS, vol. 58(1), pages 203-213, February.
    7. Hao Zhang, 2010. "Partially Observable Markov Decision Processes: A Geometric Technique and Analysis," Operations Research, INFORMS, vol. 58(1), pages 214-228, February.
    8. Zeynep Turgay & Fikri Karaesmen & E. Örmeci, 2015. "A dynamic inventory rationing problem with uncertain demand and production rates," Annals of Operations Research, Springer, vol. 231(1), pages 207-228, August.
    9. Schapaugh, Adam W. & Tyre, Andrew J., 2013. "Accounting for parametric uncertainty in Markov decision processes," Ecological Modelling, Elsevier, vol. 254(C), pages 15-21.
    10. Wolfram Wiesemann & Daniel Kuhn & Berç Rustem, 2013. "Robust Markov Decision Processes," Mathematics of Operations Research, INFORMS, vol. 38(1), pages 153-183, February.
    11. Hyeong Chang, 2006. "Perfect information two-person zero-sum markov games with imprecise transition probabilities," Mathematical Methods of Operations Research, Springer;Gesellschaft für Operations Research (GOR);Nederlands Genootschap voor Besliskunde (NGB), vol. 64(2), pages 335-351, October.
    12. Blanc, J.P.C. & den Hertog, D., 2008. "On Markov Chains with Uncertain Data," Discussion Paper 2008-50, Tilburg University, Center for Economic Research.
    13. Salah eddine Semati & Abdelkader Gasmi, 2023. "Markov interval chain (MIC) for solving a decision problem," OPSEARCH, Springer;Operational Research Society of India, vol. 60(2), pages 802-811, June.

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