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Relevant bibliographies by topics / Sequential decision processes

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Academic literature on the topic 'Sequential decision processes'

Author: Grafiati

Published: 4 June 2021

Last updated: 26 July 2025

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Journal articles on the topic "Sequential decision processes"

1

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 sign

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Sobel, Matthew J., and Wei Wei. "Myopic Solutions of Homogeneous Sequential Decision Processes." Operations Research 58, no. 4-part-2 (2010): 1235–46. http://dx.doi.org/10.1287/opre.1090.0767.

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El Chamie, Mahmoud, Dylan Janak, and Behçet Açıkmeşe. "Markov decision processes with sequential sensor measurements." Automatica 103 (May 2019): 450–60. http://dx.doi.org/10.1016/j.automatica.2019.02.026.

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Feinberg, Eugene A. "On essential information in sequential decision processes." Mathematical Methods of Operations Research 62, no. 3 (2005): 399–410. http://dx.doi.org/10.1007/s00186-005-0035-3.

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Maruyama, Yukihiro. "Strong representation theorems for bitone sequential decision processes." Optimization Methods and Software 18, no. 4 (2003): 475–89. http://dx.doi.org/10.1080/1055678031000154707.

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Milani Fard, M., and J. Pineau. "Non-Deterministic Policies in Markovian Decision Processes." Journal of Artificial Intelligence Research 40 (January 5, 2011): 1–24. http://dx.doi.org/10.1613/jair.3175.

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Markovian processes have long been used to model stochastic environments. Reinforcement learning has emerged as a framework to solve sequential planning and decision-making problems in such environments. In recent years, attempts were made to apply methods from reinforcement learning to construct decision support systems for action selection in Markovian environments. Although conventional methods in reinforcement learning have proved to be useful in problems concerning sequential decision-making, they cannot be applied in their current form to decision support systems, such as those in medica

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Maruyama, Yukihiro. "SUPER-STRONG REPRESENTATION THEOREMS FOR NONDETERMINISTIC SEQUENTIAL DECISION PROCESSES." Journal of the Operations Research Society of Japan 60, no. 2 (2017): 136–55. http://dx.doi.org/10.15807/jorsj.60.136.

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Hantula, Donald A., and Charles R. Crowell. "Intermittent Reinforcement and Escalation Processes in Sequential Decision Making:." Journal of Organizational Behavior Management 14, no. 2 (1994): 7–36. http://dx.doi.org/10.1300/j075v14n02_03.

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Canbolat, Pelin G., and Uriel G. Rothblum. "(Approximate) iterated successive approximations algorithm for sequential decision processes." Annals of Operations Research 208, no. 1 (2012): 309–20. http://dx.doi.org/10.1007/s10479-012-1073-x.

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Mahadevan, Sridhar. "Representation Discovery in Sequential Decision Making." Proceedings of the AAAI Conference on Artificial Intelligence 24, no. 1 (2010): 1718–21. http://dx.doi.org/10.1609/aaai.v24i1.7766.

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Automatically constructing novel representations of tasks from analysis of state spaces is a longstanding fundamental challenge in AI. I review recent progress on this problem for sequential decision making tasks modeled as Markov decision processes. Specifically, I discuss three classes of representation discovery problems: finding functional, state, and temporal abstractions. I describe solution techniques varying along several dimensions: diagonalization or dilation methods using approximate or exact transition models; reward-specific vs reward-invariant methods; global vs. local representa

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Dissertations / Theses on the topic "Sequential decision processes"

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Saebi, Nasrollah. "Sequential decision procedures for point processes." Thesis, Birkbeck (University of London), 1987. http://eprints.kingston.ac.uk/8409/.

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Ramsey, David Mark. "Models of evolution, interaction and learning in sequential decision processes." Thesis, University of Bristol, 1994. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.239085.

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Wang, You-Gan. "Contributions to the theory of Gittins indices : with applications in pharmaceutical research and clinical trials." Thesis, University of Oxford, 1991. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.293423.

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El, Khalfi Zeineb. "Lexicographic refinements in possibilistic sequential decision-making models." Thesis, Toulouse 3, 2017. http://www.theses.fr/2017TOU30269/document.

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Ce travail contribue à la théorie de la décision possibiliste et plus précisément à la prise de décision séquentielle dans le cadre de la théorie des possibilités, à la fois au niveau théorique et pratique. Bien qu'attrayante pour sa capacité à résoudre les problèmes de décision qualitatifs, la théorie de la décision possibiliste souffre d'un inconvénient important : les critères d'utilité qualitatives possibilistes comparent les actions avec les opérateurs min et max, ce qui entraîne un effet de noyade. Pour surmonter ce manque de pouvoir décisionnel, plusieurs raffinements ont été proposés d

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Raffensperger, Peter Abraham. "Measuring and Influencing Sequential Joint Agent Behaviours." Thesis, University of Canterbury. Electrical and Computer Engineering, 2013. http://hdl.handle.net/10092/7472.

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Algorithmically designed reward functions can influence groups of learning agents toward measurable desired sequential joint behaviours. Influencing learning agents toward desirable behaviours is non-trivial due to the difficulties of assigning credit for global success to the deserving agents and of inducing coordination. Quantifying joint behaviours lets us identify global success by ranking some behaviours as more desirable than others. We propose a real-valued metric for turn-taking, demonstrating how to measure one sequential joint behaviour. We describe how to identify the presence of tu

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Dulac-Arnold, Gabriel. "A General Sequential Model for Constrained Classification." Thesis, Paris 6, 2014. http://www.theses.fr/2014PA066572.

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Nous proposons une nouvelle approche pour l'apprentissage de représentation parcimonieuse, où le but est de limiter le nombre de caractéristiques sélectionnées \textbf{par donnée}, résultant en un modèle que nous appellerons \textit{Modèle de parcimonie locale pour la classification} --- \textit{Datum-Wise Sparse Classification} (DWSC) en anglais. Notre approche autorise le fait que les caractéristiques utilisées lors de la classification peuvent être différentes d'une donnée à une autre: une donnée facile à classifier le sera ainsi en ne considérant que quelques caractéristiques, tandis que p

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Warren, Adam L. "Sequential decision-making under uncertainty /." *McMaster only, 2004.

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Dulac-Arnold, Gabriel. "A General Sequential Model for Constrained Classification." Electronic Thesis or Diss., Paris 6, 2014. http://www.theses.fr/2014PA066572.

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Nous proposons une nouvelle approche pour l'apprentissage de représentation parcimonieuse, où le but est de limiter le nombre de caractéristiques sélectionnées \textbf{par donnée}, résultant en un modèle que nous appellerons \textit{Modèle de parcimonie locale pour la classification} --- \textit{Datum-Wise Sparse Classification} (DWSC) en anglais. Notre approche autorise le fait que les caractéristiques utilisées lors de la classification peuvent être différentes d'une donnée à une autre: une donnée facile à classifier le sera ainsi en ne considérant que quelques caractéristiques, tandis que p

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Péron, Martin Brice. "Optimal sequential decision-making under uncertainty." Thesis, Queensland University of Technology, 2018. https://eprints.qut.edu.au/120831/1/Martin%20Brice_Peron_Thesis.pdf.

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This thesis develops novel mathematical models to make optimal sequential decisions under uncertainty. One of the main objectives is to scale Markov decision processes, the framework of choice for selecting the best sequential decisions, to larger problems. The thesis is motivated by the management of the invasive tiger mosquito Aedes albopictus across the Torres Strait Islands, an archipelago of islands at the doorstep of the Australian mainland.

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Zawaideh, Zaid. "Eliciting preferences sequentially using partially observable Markov decision processes." Thesis, McGill University, 2008. http://digitool.Library.McGill.CA:80/R/?func=dbin-jump-full&object_id=18794.

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Decision Support systems have been gaining in importance recently. Yet one of the bottlenecks of designing such systems lies in understanding how the user values different decision outcomes, or more simply what the user preferences are. Preference elicitation promises to remove the guess work of designing decision making agents by providing more formal methods for measuring the `goodness' of outcomes. This thesis aims to address some of the challenges of preference elicitation such as the high dimensionality of the underlying problem. The problem is formulated as a partially observable Markov

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Books on the topic "Sequential decision processes"

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Villemeur, Étienne Billette de. Sequential decision processes make behavioural types endogenous. European University Institute, 1999.

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Villemeur, Etienne Billette de. Sequential decision processes make behavioural types endogenous. European University Institute, 1999.

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Alent'eva, Tat'yana. Public opinion in the United States on the eve of the Civil war (1850-1861), was. INFRA-M Academic Publishing LLC., 2020. http://dx.doi.org/10.12737/1068789.

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The monograph first examines American public opinion as a major factor of social and political life in the period of the maturing of the Civil war (1861-1865 gg.). Special value it is given by the study of the struggle in the South and in the North, consideration of the process of formation of two socio-cultural models.  On the wide canvas of the socio-economic and political history in the monograph analyses the state and development of public opinion in the United States, sequentially from the compromise of 1850, a small civil war in Kansas, the uprising of John brown, of the maturing o

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Stein, W., G. Burkheimer, and Anatol Rapoport. Response Models for Detection of Change. Springer, 2014.

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Stein, W., G. Burkheimer, and Anatol Rapoport. Response Models for Detection of Change. Springer London, Limited, 2012.

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Rapoport, Anatol. Response Models for Detection of Change. Springer, 2013.

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Howes, Andrew, Xiuli Chen, Aditya Acharya, and Richard L. Lewis. Interaction as an Emergent Property of a Partially Observable Markov Decision Process. Oxford University Press, 2018. http://dx.doi.org/10.1093/oso/9780198799603.003.0011.

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In this chapter we explore the potential advantages of modeling the interaction between a human and a computer as a consequence of a Partially Observable Markov Decision Process (POMDP) that models human cognition. POMDPs can be used to model human perceptual mechanisms, such as human vision, as partial (uncertain) observers of a hidden state are possible. In general, POMDPs permit a rigorous definition of interaction as the outcome of a reward maximizing stochastic sequential decision processes. They have been shown to explain interaction between a human and an environment in a range of scena

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Lepora, Nathan F. Decision making. Oxford University Press, 2018. http://dx.doi.org/10.1093/oso/9780199674923.003.0028.

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Decision making is the process by which alternatives are deliberated and chosen based on the values and goals of the decision maker. In this chapter, we describe recent progress in understanding how living organisms make decisions and the implications for engineering artificial systems with decision-making capabilities. Nature appears to re-use design principles for decision making across a hierarchy of organizational levels, from cells to organisms to entire populations. One common principle is that decision formation is realized by accumulating sensory evidence up to a threshold, approximati

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Ratcliff, Roger, and Philip Smith. Modeling Simple Decisions and Applications Using a Diffusion Model. Edited by Jerome R. Busemeyer, Zheng Wang, James T. Townsend, and Ami Eidels. Oxford University Press, 2015. http://dx.doi.org/10.1093/oxfordhb/9780199957996.013.3.

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The diffusion model is one of the major sequential-sampling models for two-choice decision-making and choice response time in psychology. The model conceives of decision-making as a process in which noisy evidence is accumulated until one of two response criteria is reached and the associated response is made. The criteria represent the amount of evidence needed to make each decision and reflect the decision maker’s response biases and speed-accuracy trade-off settings. In this chapter we examine the application of the diffusion model in a variety of different settings. We discuss the optimali

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Book chapters on the topic "Sequential decision processes"

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de Moor, Oege. "A generic program for sequential decision processes." In Programming Languages: Implementations, Logics and Programs. Springer Berlin Heidelberg, 1995. http://dx.doi.org/10.1007/bfb0026809.

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Choi, Samuel P. M., Dit-Yan Yeung, and Nevin L. Zhang. "Hidden-Mode Markov Decision Processes for Nonstationary Sequential Decision Making." In Sequence Learning. Springer Berlin Heidelberg, 2000. http://dx.doi.org/10.1007/3-540-44565-x_12.

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Loucks, Daniel P. "Modeling Stochastic Processes." In International Series in Operations Research & Management Science. Springer International Publishing, 2022. http://dx.doi.org/10.1007/978-3-030-93986-1_13.

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AbstractMany public systems must deal with uncertain inputs over time. This chapter illustrates how models incorporating uncertain inputs over time can be developed and solved. Stochastic linear and dynamic programming models are developed to show the difference in output that define optimal sequential conditional decision making strategies.

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Dvoretzky, A., J. Kiefer, and J. Wolfowitz. "Sequential Decision Problems for Processes with Continuous Time Parameter. Testing Hypotheses." In Collected Papers. Springer US, 1985. http://dx.doi.org/10.1007/978-1-4613-8505-9_10.

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Troffaes, Matthias C. M., Nathan Huntley, and Ricardo Shirota Filho. "Sequential Decision Processes under Act-State Independence with Arbitrary Choice Functions." In Communications in Computer and Information Science. Springer Berlin Heidelberg, 2010. http://dx.doi.org/10.1007/978-3-642-14055-6_11.

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Dvoretzky, A., J. Kiefer, and J. Wolfowitz. "Sequential Decision Problems for Processes with Continuous Time Parameter. Problems of Estimation." In Collected Papers. Springer US, 1985. http://dx.doi.org/10.1007/978-1-4613-8505-9_12.

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Dvoretzky, A., J. Kiefer, and J. Wolfowitz. "Corrections to “Sequential Decision Problems for Processes with Continuous Time Parameter. Testing Hypotheses”." In Collected Papers. Springer US, 1985. http://dx.doi.org/10.1007/978-1-4613-8505-9_11.

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Schmidt, Klaus D. "A Sequential Lebesgue-Radon-Nikodym Theorem and the Lebesgue Decomposition of Martingales." In Transactions of the Tenth Prague Conference on Information Theory, Statistical Decision Functions, Random Processes. Springer Netherlands, 1988. http://dx.doi.org/10.1007/978-94-010-9913-4_36.

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Junges, Sebastian, Nils Jansen, and Sanjit A. Seshia. "Enforcing Almost-Sure Reachability in POMDPs." In Computer Aided Verification. Springer International Publishing, 2021. http://dx.doi.org/10.1007/978-3-030-81688-9_28.

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AbstractPartially-Observable Markov Decision Processes (POMDPs) are a well-known stochastic model for sequential decision making under limited information. We consider the EXPTIME-hard problem of synthesising policies that almost-surely reach some goal state without ever visiting a bad state. In particular, we are interested in computing the winning region, that is, the set of system configurations from which a policy exists that satisfies the reachability specification. A direct application of such a winning region is the safe exploration of POMDPs by, for instance, restricting the behavior o

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Andriushchenko, Roman, Alexander Bork, Milan Češka, Sebastian Junges, Joost-Pieter Katoen, and Filip Macák. "Search and Explore: Symbiotic Policy Synthesis in POMDPs." In Computer Aided Verification. Springer Nature Switzerland, 2023. http://dx.doi.org/10.1007/978-3-031-37709-9_6.

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AbstractThis paper marries two state-of-the-art controller synthesis methods for partially observable Markov decision processes (POMDPs), a prominent model in sequential decision making under uncertainty. A central issue is to find a POMDP controller—that solely decides based on the observations seen so far—to achieve a total expected reward objective. As finding optimal controllers is undecidable, we concentrate on synthesising good finite-state controllers (FSCs). We do so by tightly integrating two modern, orthogonal methods for POMDP controller synthesis: a belief-based and an inductive ap

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Conference papers on the topic "Sequential decision processes"

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Servia, Miguel �ngel de Carvalho, Ilya Orson Sandoval, King Kuok (Mimi) Hii, Klaus Hellgardt, Dongda Zhang, and Ehecatl Antonio del Rio Chanona. "Physics-Informed Automated Discovery of Kinetic Models." In The 35th European Symposium on Computer Aided Process Engineering. PSE Press, 2025. https://doi.org/10.69997/sct.152436.

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The industrialization of catalytic processes requires reliable kinetic models for design, optimization, and control. While white box models are preferred for their interpretability, they demand considerable time and expertise for their construction. This research enhances the ADoK-S framework by embedding prior expert knowledge using mathematical constraints and integrating uncertainty quantification. The improved methodology consists of: (I) a genetic programming algorithm with constraints to produce physically coherent candidate models, (II) a sequential optimization algorithm for parameter

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Wallrath, Roderich, Edwin Zondervan, and Meik B. Franke. "Integration of MILP and Discrete-Event Simulation for Flowshop Scheduling Using Benders Decomposition." In The 35th European Symposium on Computer Aided Process Engineering. PSE Press, 2025. https://doi.org/10.69997/sct.180841.

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Real-world flowshop problems which are very common in the chemical industry are often difficult to solve in a reasonable time with allocation, sequencing, and lot-sizing decisions. Although great progress has been made in the last 20 years regarding MILP model formulations and solution algorithms, realistically-sized flowshop problems with resource and buffer constraints are still difficult to solve. On the other hand, discrete-event simulation (DES) allows for very detailed modelling of process plants, but lacking of optimization capabilities. Simulation Optimization (SO) combines the high-de

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Prajapat, Manish, Amon Lahr, Johannes Köhler, Andreas Krause, and Melanie N. Zeilinger. "Towards safe and tractable Gaussian process-based MPC: Efficient sampling within a sequential quadratic programming framework." In 2024 IEEE 63rd Conference on Decision and Control (CDC). IEEE, 2024. https://doi.org/10.1109/cdc56724.2024.10886350.

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Chiu, Po-Hsiang, and Manfred Huber. "Clustering Similar Actions in Sequential Decision Processes." In 2009 International Conference on Machine Learning and Applications (ICMLA). IEEE, 2009. http://dx.doi.org/10.1109/icmla.2009.98.

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Mazo, Manuel, and Ming Cao. "Design of reward structures for sequential decision-making processes using symbolic analysis." In 2013 American Control Conference (ACC). IEEE, 2013. http://dx.doi.org/10.1109/acc.2013.6580516.

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Baby, Mathew, and Anand Balu Nellippallil. "An Information-Decision Framework to Support Cooperative Decision Making in the Top-Down Design of Cyber-Physical-Manufacturing Systems." In ASME 2022 International Design Engineering Technical Conferences and Computers and Information in Engineering Conference. American Society of Mechanical Engineers, 2022. http://dx.doi.org/10.1115/detc2022-90836.

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Abstract Decision-making in the design of cyber-physical manufacturing (CPM) systems is complex due to many decisional entities and their complex interactions that need to be appropriately modeled and analyzed. One approach to designing these systems is the goal-oriented inverse design (GoID), using which satisficing design solutions are sought in a top-down manner. In this approach, entity decisions are directed towards meeting the goals propagated inversely from the subsequent entity in the manufacturing sequence. However, achieving the goals in a top-down manner may not be feasible for cert

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Kent, David, Siddhartha Banerjee, and Sonia Chernova. "Learning Sequential Decision Tasks for Robot Manipulation with Abstract Markov Decision Processes and Demonstration-Guided Exploration." In 2018 IEEE-RAS 18th International Conference on Humanoid Robots (Humanoids). IEEE, 2018. http://dx.doi.org/10.1109/humanoids.2018.8624949.

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Shi, Hanyu, and Fengqi You. "Adaptive surrogate-based algorithm for integrated scheduling and dynamic optimization of sequential batch processes." In 2015 54th IEEE Conference on Decision and Control (CDC). IEEE, 2015. http://dx.doi.org/10.1109/cdc.2015.7403372.

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Zhao, Chunhui, and Youxian Sun. "Step-wise sequential phase partition algorithm and on-line monitoring strategy for multiphase batch processes." In 2013 25th Chinese Control and Decision Conference (CCDC). IEEE, 2013. http://dx.doi.org/10.1109/ccdc.2013.6561559.

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Baek, Stanley S., Hyukseong Kwon, Josiah A. Yoder, and Daniel Pack. "Optimal path planning of a target-following fixed-wing UAV using sequential decision processes." In 2013 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS 2013). IEEE, 2013. http://dx.doi.org/10.1109/iros.2013.6696775.

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Reports on the topic "Sequential decision processes"

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Berlin, Noémie, Jan Dul, Marco Gazel, Louis Lévy-Garboua, and Todd Lubart. Creative Cognition as a Bandit Problem. CIRANO, 2023. http://dx.doi.org/10.54932/anre7929.

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This paper characterizes creative cognition as a multi-armed bandit problem involving a trade-off between exploration and exploitation in sequential decisions from experience taking place in novel uncertain environments. Creative cognition implements an efficient learning process in this kind of dynamic decision. Special emphasis is put on the optimal sequencing of divergent and convergent behavior by showing that divergence must be inhibited at one point to converge toward creative behavior so that excessive divergence is counterproductive. We test this hypothesis in two behavioral experiment

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Darling, Arthur H., and William J. Vaughan. The Optimal Sample Size for Contingent Valuation Surveys: Applications to Project Analysis. Inter-American Development Bank, 2000. http://dx.doi.org/10.18235/0008824.

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One of the first questions that has to be answered in the survey design process is "How many subjects should be interviewed?" The answer can have significant implications for the cost of project preparation, since in Latin America and the Caribbean costs per interview can range from US$20 to US$100. Traditionally, the sample size question has been answered in an unsatisfactory way by either dividing an exogenously fixed survey budget by the cost per interview or by employing some variant of a standard statistical tolerance interval formula. The answer is not to be found in the environmental ec

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