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Relevant bibliographies by topics / Sequential decision processes / Dissertations / Theses

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Dissertations / Theses on the topic 'Sequential decision processes'

Author: Grafiati

Published: 4 June 2021

Last updated: 26 July 2025

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1

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

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2

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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3

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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4

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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5

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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6

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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7

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

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8

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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9

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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10

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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11

Hoock, Jean-Baptiste. "Contributions to Simulation-based High-dimensional Sequential Decision Making." Phd thesis, Université Paris Sud - Paris XI, 2013. http://tel.archives-ouvertes.fr/tel-00912338.

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My thesis is entitled "Contributions to Simulation-based High-dimensional Sequential Decision Making". The context of the thesis is about games, planning and Markov Decision Processes. An agent interacts with its environment by successively making decisions. The agent starts from an initial state until a final state in which the agent can not make decision anymore. At each timestep, the agent receives an observation of the state of the environment. From this observation and its knowledge, the agent makes a decision which modifies the state of the environment. Then, the agent receives a reward

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12

Zhang, Zhao. "Learning Path Recommendation : A Sequential Decision Process." Electronic Thesis or Diss., Université de Lorraine, 2022. http://www.theses.fr/2022LORR0108.

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Au cours des deux dernières décennies, nous avons assisté à une adoption croissante du numérique dans le domaine de l'education. Cela est accompagné par un accroissement du nombre de ressources pédagogiques accessibles par les apprenants. Par conséquent, des systèmes de recommandation deviennent nécessaires pour aider les apprenants à trouver des ressources qui leur sont utiles. En particulier, cela inclut les systèmes de recommandation de parcours d'apprentissage qui visent par exemple à améliorer l'expérience d'apprentissage des apprenants, et notamment leur niveau de connaissance. Dans ce c

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13

Filho, Ricardo Shirota. "Processos de decisão Markovianos com probabilidades imprecisas e representações relacionais: algoritmos e fundamentos." Universidade de São Paulo, 2012. http://www.teses.usp.br/teses/disponiveis/3/3152/tde-13062013-160912/.

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Este trabalho é dedicado ao desenvolvimento teórico e algorítmico de processos de decisão markovianos com probabilidades imprecisas e representações relacionais. Na literatura, essa configuração tem sido importante dentro da área de planejamento em inteligência artificial, onde o uso de representações relacionais permite obter descrições compactas, e o emprego de probabilidades imprecisas resulta em formas mais gerais de incerteza. São três as principais contribuições deste trabalho. Primeiro, efetua-se uma discussão sobre os fundamentos de tomada de decisão sequencial com probabilidades impr

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14

Ernsberger, Timothy S. "Integrating Deterministic Planning and Reinforcement Learning for Complex Sequential Decision Making." Case Western Reserve University School of Graduate Studies / OhioLINK, 2013. http://rave.ohiolink.edu/etdc/view?acc_num=case1354813154.

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15

Couetoux, Adrien. "Monte Carlo Tree Search for Continuous and Stochastic Sequential Decision Making Problems." Thesis, Paris 11, 2013. http://www.theses.fr/2013PA112192.

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Dans cette thèse, nous avons étudié les problèmes de décisions séquentielles, avec comme application la gestion de stocks d'énergie. Traditionnellement, ces problèmes sont résolus par programmation dynamique stochastique. Mais la grande dimension, et la non convexité du problème, amènent à faire des simplifications sur le modèle pour pouvoir faire fonctionner ces méthodes.Nous avons donc étudié une méthode alternative, qui ne requiert pas de simplifications du modèle: Monte Carlo Tree Search (MCTS). Nous avons commencé par étendre le MCTS classique (qui s’applique aux domaines finis et détermi

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16

Pesquerel, Fabien. "Information per unit of interaction in stochastic sequential decision making." Electronic Thesis or Diss., Université de Lille (2022-....), 2023. https://pepite-depot.univ-lille.fr/LIBRE/EDMADIS/2023/2023ULILB048.pdf.

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Dans cette thèse, nous nous interrogeons sur la vitesse à laquelle on peut résoudre un problème stochastique inconnu.À cette fin, nous introduisons deux domaines de recherche connus sous le nom de Bandit et d'Apprentissage par Renforcement.Dans ces deux champs d'étude, un agent doit séquentiellement prendre des décisions qui affecteront un signal de récompense qu'il reçoit.L'agent ne connaît pas l'environnement avec lequel il interagit, mais pourtant souhaite maximiser sa récompense moyenne à long terme.Plus précisément, on étudie des problèmes de décision stochastique dans lesquels l'agent ch

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17

Hadoux, Emmanuel. "Markovian sequential decision-making in non-stationary environments : application to argumentative debates." Electronic Thesis or Diss., Paris 6, 2015. http://www.theses.fr/2015PA066489.

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Les problèmes de décision séquentielle dans l’incertain requièrent qu’un agent prenne des décisions, les unes après les autres, en fonction de l’état de l’environnement dans lequel il se trouve. Dans la plupart des travaux, l’environnement dans lequel évolue l’agent est supposé stationnaire, c’est-à-dire qu’il n’évolue pas avec le temps. Toute- fois, l’hypothèse de stationnarité peut ne pas être vérifiée quand, par exemple, des évènements exogènes au problème interviennent. Dans cette thèse, nous nous intéressons à la prise de décision séquentielle dans des environnements non-stationnaires. No

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18

Poolla, Radhika. "A Reinforcement Learning Approach To Obtain Treatment Strategies In Sequential Medical Decision Problems." [Tampa, Fla.] : University of South Florida, 2003. http://purl.fcla.edu/fcla/etd/SFE0000215.

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19

Hadoux, Emmanuel. "Markovian sequential decision-making in non-stationary environments : application to argumentative debates." Thesis, Paris 6, 2015. http://www.theses.fr/2015PA066489/document.

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Les problèmes de décision séquentielle dans l’incertain requièrent qu’un agent prenne des décisions, les unes après les autres, en fonction de l’état de l’environnement dans lequel il se trouve. Dans la plupart des travaux, l’environnement dans lequel évolue l’agent est supposé stationnaire, c’est-à-dire qu’il n’évolue pas avec le temps. Toute- fois, l’hypothèse de stationnarité peut ne pas être vérifiée quand, par exemple, des évènements exogènes au problème interviennent. Dans cette thèse, nous nous intéressons à la prise de décision séquentielle dans des environnements non-stationnaires. No

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20

Di, Caro Gianni. "Ant colony optimization and its application to adaptive routing in telecommunication networks." Doctoral thesis, Universite Libre de Bruxelles, 2004. http://hdl.handle.net/2013/ULB-DIPOT:oai:dipot.ulb.ac.be:2013/211149.

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In ant societies, and, more in general, in insect societies, the activities of the individuals, as well as of the society as a whole, are not regulated by any explicit form of centralized control. On the other hand, adaptive and robust behaviors transcending the behavioral repertoire of the single individual can be easily observed at society level. These complex global behaviors are the result of self-organizing dynamics driven by local interactions and communications among a number of relatively simple individuals.<p><p>The simultaneous presence of these and other fascinating and unique chara

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21

Li, Yongchang. "An Intelligent, Knowledge-based Multiple Criteria Decision Making Advisor for Systems Design." Diss., Georgia Institute of Technology, 2007. http://hdl.handle.net/1853/14559.

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Aerospace systems are complex systems with interacting disciplines and technologies. As a result, the Decision Makers (DMs) dealing with such problems are involved in balancing the multiple, potentially conflicting attributes/criteria, transforming a large amount of customer supplied guidelines into a solidly defined set of requirement definitions. A variety of existing decision making methods are available to deal with this type of decision problems. The selection of a most appropriate decision making method is of particular importance since inappropriate decision methods are likely causes of

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22

Wei, Wei. "Stochastic Dynamic Optimization and Games in Operations Management." Case Western Reserve University School of Graduate Studies / OhioLINK, 2013. http://rave.ohiolink.edu/etdc/view?acc_num=case1354751981.

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23

Santos, Hugo Henrique Kegler dos. "Procedimentos sequenciais Bayesianos aplicados ao processo de captura-recaptura." Universidade Federal de São Carlos, 2014. https://repositorio.ufscar.br/handle/ufscar/4494.

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Made available in DSpace on 2016-06-02T20:04:52Z (GMT). No. of bitstreams: 1 6306.pdf: 1062380 bytes, checksum: de31a51e2d0a59e52556156a08c37b41 (MD5) Previous issue date: 2014-05-30<br>Financiadora de Estudos e Projetos<br>In this work, we make a study of the Bayes sequential decision procedure applied to capture-recapture with fixed sample sizes, to estimate the size of a finite and closed population process. We present the statistical model, review the Bayesian decision theory, presenting the pure decision problem, the statistical decision problem and the sequential decision procedure. We

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24

Junyent, Barbany Miquel. "Width-Based Planning and Learning." Doctoral thesis, Universitat Pompeu Fabra, 2021. http://hdl.handle.net/10803/672779.

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Optimal sequential decision making is a fundamental problem to many diverse fields. In recent years, Reinforcement Learning (RL) methods have experienced unprecedented success, largely enabled by the use of deep learning models, reaching human-level performance in several domains, such as the Atari video games or the ancient game of Go. In contrast to the RL approach in which the agent learns a policy from environment interaction samples, ignoring the structure of the problem, the planning approach for decision making assumes known models for the agent's goals and domain dynamics, and fo

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25

Grand-Clement, Julien. "Robust and Interpretable Sequential Decision-Making for Healthcare." Thesis, 2021. https://doi.org/10.7916/d8-maqq-mp30.

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Markov Decision Processes (MDP) is a common framework for modeling sequential decision-making problems, with applications ranging from inventory and supply chains to healthcare applications, autonomous driving and solving repeated games. Despite its modeling power, two fundamental challenges arise when using the MDP framework in real-worldapplications. First, the optimal decision rule may be highly dependent on the MDP parameters (e.g., transition rates across states and rewards for each state-action pair). When the parameters are miss-estimated, the resulting decision rule may be suboptimal w

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26

Kinathil, Shamin. "Closed-form Solutions to Sequential Decision Making within Markets." Phd thesis, 2018. http://hdl.handle.net/1885/186490.

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Sequential decision making is a pervasive and inescapable requirement of every day life. Deciding upon which sequence of actions to take is complicated by incomplete information about the environment, the effects of each decision upon the future state of the environment, ill-defined objectives and our own cognitive limitations. These challenges are exacerbated in financial markets which are in a constant state of flux, with prices adjusting to new information, winning traders replacing losing traders and the introduction of new technologies. Decision theoretic planning provides powerful and fl

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27

Warn, Nathan. "Sequential effects in simple decision making: testing proposed mechanisms and identifying candidate processes for individual differences." Thesis, 2021. http://hdl.handle.net/1959.13/1428303.

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Research Doctorate - Doctor of Philosophy (PhD)<br>Sequential effects have emerged as a ubiquitous facet of choice-reaction time experiments, and refer to the systematic variation in reaction time and accuracy as a function of the preceding trial sequence. The aim of this thesis is to contribute to the resolution of some ambiguities in the sequential effects literature, and expand our understanding of sequential effects by examining how they manifest in previously unexplored tasks, and different experimental conditions. The specific questions that will be addressed in this thesis relate to (1)

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28

Khan, Omar Zia. "Policy Explanation and Model Refinement in Decision-Theoretic Planning." Thesis, 2013. http://hdl.handle.net/10012/7808.

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Decision-theoretic systems, such as Markov Decision Processes (MDPs), are used for sequential decision-making under uncertainty. MDPs provide a generic framework that can be applied in various domains to compute optimal policies. This thesis presents techniques that offer explanations of optimal policies for MDPs and then refine decision theoretic models (Bayesian networks and MDPs) based on feedback from experts. Explaining policies for sequential decision-making problems is difficult due to the presence of stochastic effects, multiple possibly competing objectives and long-range effects of

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29

Narayanaprasad, Karthik Periyapattana. "Sequential Controlled Sensing to Detect an Anomalous Process." Thesis, 2021. https://etd.iisc.ac.in/handle/2005/5514.

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In this thesis, we study the problem of identifying an anomalous arm in a multi-armed bandit as quickly as possible, subject to an upper bound on the error probability. Also known as odd arm identification, this problem falls within the class of optimal stopping problems in decision theory and can be embedded within the framework of active sequential hypothesis testing. Prior works on odd arm identification dealt with independent and identically distributed observations from each arm. We provide the first known extension to the case of Markov observations from each arm. Our analysis and result

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