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Journal articles on the topic 'Processos de decisió de Markov'

Author: Grafiati

Published: 18 April 2022

Last updated: 18 July 2025

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1

Pellegrini, Jerônimo, and Jacques Wainer. "Processos de Decisão de Markov: um tutorial." Revista de Informática Teórica e Aplicada 14, no. 2 (2007): 133–79. http://dx.doi.org/10.22456/2175-2745.5694.

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2

Trindade, Anderson Laécio Galindo, Linda Lee Ho, and Roberto da Costa Quinino. "Controle on-line por atributos com erros de classificação: uma abordagem econômica com classificações repetidas." Pesquisa Operacional 27, no. 1 (2007): 105–16. http://dx.doi.org/10.1590/s0101-74382007000100006.

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O procedimento de controle on-line de processos por atributos, proposto por Taguchi et al. (1989), consiste em amostrar um item a cada m produzidos e decidir, a cada inspeção, se houve ou não a redução da fração de itens conformes produzidos. Caso o item inspecionado for não conforme, pára-se o processo para ajuste. Como o sistema de inspeção pode estar sujeito a erros de classificação, desenvolve-se um modelo probabilístico que considera classificações repetidas e independentes do item amostrado em um sistema de controle com inspeção imperfeita. Utilizando-se as propriedades de uma cadeia de Markov ergódica, obtém-se uma expressão do custo médio do sistema de controle, que pode ser minimizada por três parâmetros: o intervalo entre inspeções; o número de classificações repetidas; e o número mínimo de classificações conformes (dentre as classificações repetidas), para julgar um item como conforme. Um exemplo numérico ilustra o procedimento proposto.

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3

Soárez, Patrícia Coelho de, Marta Oliveira Soares, and Hillegonda Maria Dutilh Novaes. "Modelos de decisão para avaliações econômicas de tecnologias em saúde." Ciência & Saúde Coletiva 19, no. 10 (2014): 4209–22. http://dx.doi.org/10.1590/1413-812320141910.02402013.

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A maioria das avaliações econômicas que participam dos processos de decisão de incorporação e financiamento de tecnologias dos sistemas de saúde utiliza modelos de decisão para avaliar os custos e benefícios das estratégias comparadas. Apesar do grande número de avaliações econômicas conduzidas no Brasil, há necessidade de aprofundamento metodológico sobre os tipos de modelos de decisão e sua aplicabilidade no nosso meio. O objetivo desta revisão de literatura é contribuir para o conhecimento e o uso de modelos de decisão nos contextos nacionais das avaliações econômicas de tecnologias em saúde. Este artigo apresenta definições gerais sobre modelos e preocupações com o seu uso; descreve os principais modelos: árvore de decisão, Markov, microssimulação, simulação de eventos discretos e dinâmicos; discute os elementos envolvidos na escolha do modelo; e exemplifica os modelos abordados com estudos de avaliação econômica nacionais de tecnologias preventivas e de programas de saúde, diagnósticas e terapêuticas.

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4

Whittle, P., and M. L. Puterman. "Markov Decision Processes." Journal of the Royal Statistical Society. Series A (Statistics in Society) 158, no. 3 (1995): 636. http://dx.doi.org/10.2307/2983459.

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5

Smith, J. Q., and D. J. White. "Markov Decision Processes." Journal of the Royal Statistical Society. Series A (Statistics in Society) 157, no. 1 (1994): 164. http://dx.doi.org/10.2307/2983520.

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6

Thomas, L. C., D. J. White, and Martin L. Puterman. "Markov Decision Processes." Journal of the Operational Research Society 46, no. 6 (1995): 792. http://dx.doi.org/10.2307/2584317.

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7

Bäuerle, Nicole, and Ulrich Rieder. "Markov Decision Processes." Jahresbericht der Deutschen Mathematiker-Vereinigung 112, no. 4 (2010): 217–43. http://dx.doi.org/10.1365/s13291-010-0007-2.

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8

Wal, J., and J. Wessels. "MARKOV DECISION PROCESSES." Statistica Neerlandica 39, no. 2 (1985): 219–33. http://dx.doi.org/10.1111/j.1467-9574.1985.tb01140.x.

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9

Thomas, L. C. "Markov Decision Processes." Journal of the Operational Research Society 46, no. 6 (1995): 792–93. http://dx.doi.org/10.1057/jors.1995.110.

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10

Brooks, Stephen, and D. J. White. "Markov Decision Processes." Statistician 44, no. 2 (1995): 292. http://dx.doi.org/10.2307/2348465.

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11

White, Chelsea C., and Douglas J. White. "Markov decision processes." European Journal of Operational Research 39, no. 1 (1989): 1–16. http://dx.doi.org/10.1016/0377-2217(89)90348-2.

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12

Baykal-Gürsoy, M., and K. Gürsoy. "SEMI-MARKOV DECISION PROCESSES." Probability in the Engineering and Informational Sciences 21, no. 4 (2007): 635–57. http://dx.doi.org/10.1017/s026996480700037x.

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Considered are semi-Markov decision processes (SMDPs) with finite state and action spaces. We study two criteria: the expected average reward per unit time subject to a sample path constraint on the average cost per unit time and the expected time-average variability. Under a certain condition, for communicating SMDPs, we construct (randomized) stationary policies that are ε-optimal for each criterion; the policy is optimal for the first criterion under the unichain assumption and the policy is optimal and pure for a specific variability function in the second criterion. For general multichain SMDPs, by using a state space decomposition approach, similar results are obtained.

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13

Sabbadin, Régis. "Possibilistic Markov decision processes." Engineering Applications of Artificial Intelligence 14, no. 3 (2001): 287–300. http://dx.doi.org/10.1016/s0952-1976(01)00007-0.

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14

Even-Dar, Eyal, Sham M. Kakade, and Yishay Mansour. "Online Markov Decision Processes." Mathematics of Operations Research 34, no. 3 (2009): 726–36. http://dx.doi.org/10.1287/moor.1090.0396.

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15

Wiesemann, Wolfram, Daniel Kuhn, and Berç Rustem. "Robust Markov Decision Processes." Mathematics of Operations Research 38, no. 1 (2013): 153–83. http://dx.doi.org/10.1287/moor.1120.0566.

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16

Glazebrook, K. D. "Competing Markov decision processes." Annals of Operations Research 29, no. 1 (1991): 537–63. http://dx.doi.org/10.1007/bf02283613.

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17

Park, YeBon, Jae chan Lee, and Jea-Hyeon Jeong. "Environmentally Friendly Fertilizer Management Strategies Using Markov Decision Process." Journal of Innovation Industry Technology 1, no. 2 (2023): 1–6. http://dx.doi.org/10.60032/jiit.2023.1.2.1.

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18

Bäuerle, Nicole, and Ulrich Rieder. "Markov decision processes under ambiguity." Banach Center Publications 122 (2020): 25–39. http://dx.doi.org/10.4064/bc122-2.

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19

Wijnmalen, Diederik J. D. "Applications of Markov Decision Processes." Journal of the Operational Research Society 45, no. 5 (1994): 607. http://dx.doi.org/10.2307/2584406.

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20

Hyeong Soo Chang, P. J. Fard, S. I. Marcus, and M. Shayman. "Multitime scale markov decision processes." IEEE Transactions on Automatic Control 48, no. 6 (2003): 976–87. http://dx.doi.org/10.1109/tac.2003.812782.

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21

Ning, Jie, and Matthew J. Sobel. "Easy Affine Markov Decision Processes." Operations Research 67, no. 6 (2019): 1719–37. http://dx.doi.org/10.1287/opre.2018.1836.

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22

Ahn, Hyun-Soo, and Rhonda Righter. "Multi-Actor Markov Decision Processes." Journal of Applied Probability 42, no. 1 (2005): 15–26. http://dx.doi.org/10.1239/jap/1110381367.

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We give a very general reformulation of multi-actor Markov decision processes and show that there is a tendency for the actors to take the same action whenever possible. This considerably reduces the complexity of the problem, either facilitating numerical computation of the optimal policy or providing a basis for a heuristic.

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23

Wijnmalen, Diederik J. D. "Applications of Markov Decision Processes." Journal of the Operational Research Society 45, no. 5 (1994): 607–8. http://dx.doi.org/10.1057/jors.1994.98.

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24

Borkar, Vivek, and Rahul Jain. "Risk-Constrained Markov Decision Processes." IEEE Transactions on Automatic Control 59, no. 9 (2014): 2574–79. http://dx.doi.org/10.1109/tac.2014.2309262.

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25

Ahn, Hyun-Soo, and Rhonda Righter. "Multi-Actor Markov Decision Processes." Journal of Applied Probability 42, no. 01 (2005): 15–26. http://dx.doi.org/10.1017/s0021900200000024.

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We give a very general reformulation of multi-actor Markov decision processes and show that there is a tendency for the actors to take the same action whenever possible. This considerably reduces the complexity of the problem, either facilitating numerical computation of the optimal policy or providing a basis for a heuristic.

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26

Givan, Robert, Sonia Leach, and Thomas Dean. "Bounded-parameter Markov decision processes." Artificial Intelligence 122, no. 1-2 (2000): 71–109. http://dx.doi.org/10.1016/s0004-3702(00)00047-3.

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27

Haviv, Moshe. "On constrained Markov decision processes." Operations Research Letters 19, no. 1 (1996): 25–28. http://dx.doi.org/10.1016/0167-6377(96)00003-x.

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28

Chen, Ming, Jerzy A. Filar, and Ke Liu. "Semi-infinite Markov decision processes." Mathematical Methods of Operations Research (ZOR) 51, no. 1 (2000): 115–37. http://dx.doi.org/10.1007/s001860050006.

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29

Xu, Huan, and Shie Mannor. "Distributionally Robust Markov Decision Processes." Mathematics of Operations Research 37, no. 2 (2012): 288–300. http://dx.doi.org/10.1287/moor.1120.0540.

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30

Filar, Jerzy A., L. C. M. Kallenberg, and Huey-Miin Lee. "Variance-Penalized Markov Decision Processes." Mathematics of Operations Research 14, no. 1 (1989): 147–61. http://dx.doi.org/10.1287/moor.14.1.147.

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31

Baykal-Gürsoy, Melike, and Keith W. Ross. "Variability Sensitive Markov Decision Processes." Mathematics of Operations Research 17, no. 3 (1992): 558–71. http://dx.doi.org/10.1287/moor.17.3.558.

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32

Fujita, Toshiharu, and Akifumi Kira. "Mutually Dependent Markov Decision Processes." Journal of Advanced Computational Intelligence and Intelligent Informatics 18, no. 6 (2014): 992–98. http://dx.doi.org/10.20965/jaciii.2014.p0992.

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In this paper, we introduce a basic framework for mutually dependent Markov decision processes (MDMDP) showing recursive mutual dependence. Our model is structured upon two types of finite-stage Markov decision processes. At each stage, the reward in one process is given by the optimal value of the alternative process problem, whose initial state is determined by the current state and decision in the original process. We formulate the MDMDP model and derive mutually dependent recursive equations by dynamic programming. Furthermore, MDMDP is illustrated in a numerical example. The model enables easier treatment of some classes of complex multi-stage decision processes.

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33

Yu, Jia Yuan, Shie Mannor, and Nahum Shimkin. "Markov Decision Processes with Arbitrary Reward Processes." Mathematics of Operations Research 34, no. 3 (2009): 737–57. http://dx.doi.org/10.1287/moor.1090.0397.

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34

Hölzl, Johannes. "Markov Chains and Markov Decision Processes in Isabelle/HOL." Journal of Automated Reasoning 59, no. 3 (2016): 345–87. http://dx.doi.org/10.1007/s10817-016-9401-5.

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35

Alagoz, Oguzhan, Heather Hsu, Andrew J. Schaefer, and Mark S. Roberts. "Markov Decision Processes: A Tool for Sequential Decision Making under Uncertainty." Medical Decision Making 30, no. 4 (2009): 474–83. http://dx.doi.org/10.1177/0272989x09353194.

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We provide a tutorial on the construction and evaluation of Markov decision processes (MDPs), which are powerful analytical tools used for sequential decision making under uncertainty that have been widely used in many industrial and manufacturing applications but are underutilized in medical decision making (MDM). We demonstrate the use of an MDP to solve a sequential clinical treatment problem under uncertainty. Markov decision processes generalize standard Markov models in that a decision process is embedded in the model and multiple decisions are made over time. Furthermore, they have significant advantages over standard decision analysis. We compare MDPs to standard Markov-based simulation models by solving the problem of the optimal timing of living-donor liver transplantation using both methods. Both models result in the same optimal transplantation policy and the same total life expectancies for the same patient and living donor. The computation time for solving the MDP model is significantly smaller than that for solving the Markov model. We briefly describe the growing literature of MDPs applied to medical decisions.

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36

Ortega-Gutiérrez, R. Israel, and H. Cruz-Suárez. "A Moreau-Yosida regularization for Markov decision processes." Proyecciones (Antofagasta) 40, no. 1 (2020): 117–37. http://dx.doi.org/10.22199/issn.0717-6279-2021-01-0008.

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This paper addresses a class of sequential optimization problems known as Markov decision processes. These kinds of processes are considered on Euclidean state and action spaces with the total expected discounted cost as the objective function. The main goal of the paper is to provide conditions to guarantee an adequate Moreau-Yosida regularization for Markov decision processes (named the original process). In this way, a new Markov decision process that conforms to the Markov control model of the original process except for the cost function induced via the Moreau-Yosida regularization is established. Compared to the original process, this new discounted Markov decision process has richer properties, such as the differentiability of its optimal value function, strictly convexity of the value function, uniqueness of optimal policy, and the optimal value function and the optimal policy of both processes, are the same. To complement the theory presented, an example is provided.

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37

Ortega-Gutiérrez, R. Israel, and H. Cruz-Suárez. "A Moreau-Yosida regularization for Markov decision processes." Proyecciones (Antofagasta) 40, no. 1 (2020): 117–37. http://dx.doi.org/10.22199/issn.0717-6279-2021-01-0008.

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This paper addresses a class of sequential optimization problems known as Markov decision processes. These kinds of processes are considered on Euclidean state and action spaces with the total expected discounted cost as the objective function. The main goal of the paper is to provide conditions to guarantee an adequate Moreau-Yosida regularization for Markov decision processes (named the original process). In this way, a new Markov decision process that conforms to the Markov control model of the original process except for the cost function induced via the Moreau-Yosida regularization is established. Compared to the original process, this new discounted Markov decision process has richer properties, such as the differentiability of its optimal value function, strictly convexity of the value function, uniqueness of optimal policy, and the optimal value function and the optimal policy of both processes, are the same. To complement the theory presented, an example is provided.

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38

Esponda, Ignacio, and Demian Pouzo. "Equilibrium in misspecified Markov decision processes." Theoretical Economics 16, no. 2 (2021): 717–57. http://dx.doi.org/10.3982/te3843.

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We provide an equilibrium framework for modeling the behavior of an agent who holds a simplified view of a dynamic optimization problem. The agent faces a Markov decision process, where a transition probability function determines the evolution of a state variable as a function of the previous state and the agent's action. The agent is uncertain about the true transition function and has a prior over a set of possible transition functions; this set reflects the agent's (possibly simplified) view of her environment and may not contain the true function. We define an equilibrium concept and provide conditions under which it characterizes steady‐state behavior when the agent updates her beliefs using Bayes' rule.

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39

Kurano, Masami. "Learning algorithms for Markov decision processes." Journal of Applied Probability 24, no. 1 (1987): 270–76. http://dx.doi.org/10.2307/3214080.

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This study is concerned with finite Markov decision processes whose dynamics and reward structure are unknown but the state is observable exactly.We establish a learning algorithm which yields an optimal policy and construct an adaptive policy which is optimal under the average expected reward criterion.

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40

Belousov, Boris, and Jan Peters. "Entropic Regularization of Markov Decision Processes." Entropy 21, no. 7 (2019): 674. http://dx.doi.org/10.3390/e21070674.

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An optimal feedback controller for a given Markov decision process (MDP) can in principle be synthesized by value or policy iteration. However, if the system dynamics and the reward function are unknown, a learning agent must discover an optimal controller via direct interaction with the environment. Such interactive data gathering commonly leads to divergence towards dangerous or uninformative regions of the state space unless additional regularization measures are taken. Prior works proposed bounding the information loss measured by the Kullback–Leibler (KL) divergence at every policy improvement step to eliminate instability in the learning dynamics. In this paper, we consider a broader family of f-divergences, and more concretely α -divergences, which inherit the beneficial property of providing the policy improvement step in closed form at the same time yielding a corresponding dual objective for policy evaluation. Such entropic proximal policy optimization view gives a unified perspective on compatible actor-critic architectures. In particular, common least-squares value function estimation coupled with advantage-weighted maximum likelihood policy improvement is shown to correspond to the Pearson χ 2 -divergence penalty. Other actor-critic pairs arise for various choices of the penalty-generating function f. On a concrete instantiation of our framework with the α -divergence, we carry out asymptotic analysis of the solutions for different values of α and demonstrate the effects of the divergence function choice on common standard reinforcement learning problems.

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41

Gopalan, Nakul, Marie DesJardins, Michael Littman, et al. "Planning with Abstract Markov Decision Processes." Proceedings of the International Conference on Automated Planning and Scheduling 27 (June 5, 2017): 480–88. http://dx.doi.org/10.1609/icaps.v27i1.13867.

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Robots acting in human-scale environments must plan under uncertainty in large state–action spaces and face constantly changing reward functions as requirements and goals change. Planning under uncertainty in large state–action spaces requires hierarchical abstraction for efficient computation. We introduce a new hierarchical planning framework called Abstract Markov Decision Processes (AMDPs) that can plan in a fraction of the time needed for complex decision making in ordinary MDPs. AMDPs provide abstract states, actions, and transition dynamics in multiple layers above a base-level “flat” MDP. AMDPs decompose problems into a series of subtasks with both local reward and local transition functions used to create policies for subtasks. The resulting hierarchical planning method is independently optimal at each level of abstraction, and is recursively optimal when the local reward and transition functions are correct. We present empirical results showing significantly improved planning speed, while maintaining solution quality, in the Taxi domain and in a mobile-manipulation robotics problem. Furthermore, our approach allows specification of a decision-making model for a mobile-manipulation problem on a Turtlebot, spanning from low-level control actions operating on continuous variables all the way up through high-level object manipulation tasks.

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42

Vanhée, Loïs, Laurent Jeanpierre, and Abdel-Illah Mouaddib. "Augmenting Markov Decision Processes with Advising." Proceedings of the AAAI Conference on Artificial Intelligence 33 (July 17, 2019): 2531–38. http://dx.doi.org/10.1609/aaai.v33i01.33012531.

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This paper introduces Advice-MDPs, an expansion of Markov Decision Processes for generating policies that take into consideration advising on the desirability, undesirability, and prohibition of certain states and actions. AdviceMDPs enable the design of designing semi-autonomous systems (systems that require operator support for at least handling certain situations) that can efficiently handle unexpected complex environments. Operators, through advising, can augment the planning model for covering unexpected real-world irregularities. This advising can swiftly augment the degree of autonomy of the system, so it can work without subsequent human intervention. This paper details the Advice-MDP formalism, a fast AdviceMDP resolution algorithm, and its applicability for real-world tasks, via the design of a professional-class semi-autonomous robot system ready to be deployed in a wide range of unexpected environments and capable of efficiently integrating operator advising.

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43

Ahmadi, Mohamadreza, Ugo Rosolia, Michel D. Ingham, Richard M. Murray, and Aaron D. Ames. "Constrained Risk-Averse Markov Decision Processes." Proceedings of the AAAI Conference on Artificial Intelligence 35, no. 13 (2021): 11718–25. http://dx.doi.org/10.1609/aaai.v35i13.17393.

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We consider the problem of designing policies for Markov decision processes (MDPs) with dynamic coherent risk objectives and constraints. We begin by formulating the problem in a Lagrangian framework. Under the assumption that the risk objectives and constraints can be represented by a Markov risk transition mapping, we propose an optimization-based method to synthesize Markovian policies that lower-bound the constrained risk-averse problem. We demonstrate that the formulated optimization problems are in the form of difference convex programs (DCPs) and can be solved by the disciplined convex-concave programming (DCCP) framework. We show that these results generalize linear programs for constrained MDPs with total discounted expected costs and constraints. Finally, we illustrate the effectiveness of the proposed method with numerical experiments on a rover navigation problem involving conditional-value-at-risk (CVaR) and entropic-value-at-risk (EVaR) coherent risk measures.

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44

IIDA, Tetsuo, and Masao Mori. "MARKOV DECISION PROCESSES WITH RANDOM HORIZON." Journal of the Operations Research Society of Japan 39, no. 4 (1996): 592–603. http://dx.doi.org/10.15807/jorsj.39.592.

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45

Doyen, Laurent, Thierry Massart, and Mahsa Shirmohammadi. "Synchronizing Objectives for Markov Decision Processes." Electronic Proceedings in Theoretical Computer Science 50 (February 18, 2011): 61–75. http://dx.doi.org/10.4204/eptcs.50.5.

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46

Adlakha, S., S. Lall, and A. Goldsmith. "Networked Markov Decision Processes With Delays." IEEE Transactions on Automatic Control 57, no. 4 (2012): 1013–18. http://dx.doi.org/10.1109/tac.2011.2168111.

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47

Bray, Robert L. "Markov Decision Processes with Exogenous Variables." Management Science 65, no. 10 (2019): 4598–606. http://dx.doi.org/10.1287/mnsc.2018.3158.

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48

Lovejoy, William S. "Policy Bounds for Markov Decision Processes." Operations Research 34, no. 4 (1986): 630–37. http://dx.doi.org/10.1287/opre.34.4.630.

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49

White, Douglas J. "Real Applications of Markov Decision Processes." Interfaces 15, no. 6 (1985): 73–83. http://dx.doi.org/10.1287/inte.15.6.73.

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50

Zhiyuan Ren and B. H. Krogh. "Markov decision Processes with fractional costs." IEEE Transactions on Automatic Control 50, no. 5 (2005): 646–50. http://dx.doi.org/10.1109/tac.2005.846520.

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