Global ETD Search
Search theses and dissertations gathered from participating repositories worldwide. Every result links back to the library that holds it. No account is needed.
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Showing 1 to 20 of 78 for “"Decision-making problems"”.
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Parameterized sequential decision making problems
Submission published under a 24 month embargo labeled 'Closed Access', the embargo will last until 2023-12-01
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The exploration-exploitation trade-off in sequential decision making problems
Sequential decision making problems require an agent to repeatedly choose between a series of actions. Common to such problems is the exploration-exploitation trade-off, where an agent must choose between the action expected to yield the best reward (exploitation) or trying an alternative action …
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Decision support systems for solving discrete multicriteria decision making problems
… the design and implementation of an interactive decision support system, assisting a single decision maker in reaching a satisfactory decision when faced by a multicriteria decision making problem. There are clearly two components involved in designing such a system, namely the concept of …
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The interplay between information and control theory within interactive decision-making problems
The context for this work is two-agent team decision systems. An agent is an intelligent entity that can measure some aspect of its environment, process information and possibly influence the environment through its action. In a colloborative two-agent team decision system, the agents can be …
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Optimal control reformulation for the solution of decision-making problems in chemical engineering
… the challenges that arise from the solution of decision-making problems in terms of convergence, efficiency and robustness. State-of-the-art solvers could fail to find the optimal solution or solve small instances of these problems. The original contribution of the current research consists of …
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Theoretical Foundations for Learning in Games and Dynamic Environments
Decision-making problems lie at the heart of numerous aspects of human and algorithmic behavior across our society, ranging from healthcare systems to financial systems to interactions with the physical world. A central challenge that arises across many decision-making problems is the presence of …
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Efficient Algorithms for High-Dimensional Data-Driven Sequential Decision-Making
The general framework of sequential decision-making captures various important real-world applications ranging from pricing, inventory control to public healthcare and pandemic management. It is central to operations research/operations management, often boiling down to solving stochastic dynamic …
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Rank reversal properties of multicriteria decision making models
Decision making problems in modern society are very important however complex. Therefore, they require strong solving techniques to handle. The AHP method attracts a lot attention for its advantages and has a very well structured methodology, while the PROMETHEE method of the European school is …
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Resource allocation problems in stochastic sequential decision making
In this thesis, we study resource allocation problems that arise in the context of stochastic sequential decision making problems. The practical utility of optimal algorithms for these problems is limited due to their high computational and storage requirements. Also, an increasing number of …
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New Models qnd Algorithms for Bandits and Markets
… markets, we consider large-scale sequential decision making problems in which a learner must deploy an algorithm to behave optimally under uncertainty. Although many of these problems can be modeled as contextual bandit problems, we argue that the tools and techniques for analyzing bandit …
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Several Reinforcement Learning Methods in Mean-Field Games with Binary Action Spaces
… enable agents to learn and solve sequential decision-making problems through accumulating rewards with delays. Despite much success in single-player settings, reinforcement learning in multi-agent domains remains a challenging task in many aspects. In this thesis, the mean-field approach will …
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Deterministic and Stochastic Bellman's Optimality Principles on Isolated Time Domains and Their Applications in Finance
… during the 1950s, by Richard Bellman to describe decision making problems. By 1952, he refined this to the modern meaning, referring specifically to nesting smaller decision problems inside larger decisions. Also, the Bellman equation, one of the basic concepts in dynamic programming, is named …
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The effect of abstract versus concrete thinking on decision-making in depression
… is the tendency to experience difficulties with decision-making. This thesis investigated whether: (i) abstract thinking is associated with decision-making problems, and (ii) inducing a converse more adaptive style of thinking, namely concrete thinking, could lead to more constructive outcomes in …
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Tracking eye behavior in decision-making maze problems
… impact a patient's ability to solve everyday problems. The ability to detect early signs of mental decline is crucial for determining whether someone might be at risk for these diseases. Eye behavior is often correlated to cognitive load, so examining the behavior of the eyes during …
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A method to establish non-informative prior probabilities for risk-based decision analysis
In Bayesian decision analysis, uncertainty and risk are accounted for with probabilities for the possible states, or states of nature, that affect the outcome of a decision. Application of Bayes’ theorem requires non-informative prior probabilities, which represent the probabilities of states of …
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Data-Driven Dynamic Decision Making: Algorithms, Structures, and Complexity Analysis
… the theory and practice of data-driven dynamic decision making, by synergizing ideas from machine learning and operations research. Throughout this thesis, we focus on three aspects: (i) developing new, practical algorithms that systematically empower data-driven dynamic decision making, (ii) …
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Near-Optimal Learning in Sequential Games
Decision making is ubiquitous, and some problems become particularly challenging due to their sequential nature, where later decisions depend on earlier ones. While humans have been attempting to solve sequential decision making problems for a long time, modern computational and machine learning …
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Efficient Reinforcement Learning for Control
… promising solutions to a wide range of dynamic decision-making problems. However, the application of RL to real-world control systems is often hindered by computational inefficiencies, scalability issues, and a lack of structure in learning mech- anisms. This thesis explores a central question: …
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Modeling and Analysis of Multilayer Complex Distribution System: A Multi-Agent Simulation for Decision Making
… almost as in detail as designers want, complex decision making problems encountered in the emerging distribution system can be implemented, learned, and evaluated through predesigned MAS models; evolutions of complex multilayer system and interactions inside can be captured with utmost …
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Balancing Teacher Following and Reward Maximization in Reinforcement Learning
… established approaches for solving sequential decision-making problems. To combine the benefits of these different forms of learning, it is common to train a policy to maximize a combination of reinforcement and teacher-student learning objectives. However, without a principled method to …
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