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.
Results
Showing 1 to 20 of 104 for “"sequential decision-making"”.
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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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Principled exploration in sequential decision-making
Submission published under a 24 month embargo labeled 'U of I Access', the embargo will last until 2026-05-01
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Algorithmic Fairness in Sequential Decision Making
… a gap in translating predictions to a justified decision. Moreover, even a justified and fair decision could lead to undesirable consequences when decisions create a feedback effect. While numerous solutions have been proposed for achieving fairness in one-shot decision-making, there is a gap in …
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Robust sequential decision-making on networks
In this thesis, I consider the research problem of designing optimal algorithms for two specific settings of the stochastic multi-armed bandit problem. The first setting considers the problem where rewards are drawn from a family of extremely heavy-tailed distributions known as a-stable …
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Sequential decision making in artificial musical intelligence
… hasn't been sufficiently studied is that of sequential decision making in musical intelligence. This thesis strives to answer the following question: Can a sequential decision making perspective guide us in the creation of better music agents, and social agents in general? And if so, how? …
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Sequential decision making with feature-linear models
This thesis is concerned with the problem of sequential decision making, where an agent interacts sequentially with an unknown environment and aims to maximise the sum of the rewards it receives. Our focus is on methods that model the reward as linear in some feature space. We consider a bandit …
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Sequential Decision Making For Choice Functions On Gambles
… preference and uncertainty models. For single decisions, applying a choice function is straightforward. In sequential problems, where the subject has multiple decision points, it is less easy. One possibility, called a normal form solution, is to list all available strategies (specifications of …
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Advances in Active Learning and Sequential Decision Making
… We address these data limitations by adopting sequential decision-making strategies, which iterate between collecting new data and making informed decisions based on newly acquired evidence. First, we tackle the problem of how to efficiently collect batches of labels when the cost of acquiring …
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Resource allocation problems in stochastic sequential decision making
… 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 applications require a decentralized solution. We …
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Advancing Efficiency and Safety in Autonomous Sequential Decision Making
… learning (RL) has significantly transformed decision making in autonomous systems. However, its practical deployment faces substantial obstacles, chiefly in achieving sample (data) efficiency and ensuring agent safety in unpredictable, dynamic environments. Additionally, the inherent partial …
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Sequential Decision-making Under Uncertainty: Novel Methodologies and Applications
In sequential decision-making under uncertainty, multistage stochastic mixed-integer programming (MSMIP) is a tool for addressing optimization problems with a given probability distribution and the goal of optimizing a performance measure over a planning horizon. If there is no knowledge about the …
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Data-Driven sequential decision making with learning under ambiguity
Markov decision processes are often used to model sequential decision-making problems in uncertain dynamic environments, such as equipment maintenance and replacement problems, and inventory control problems. The objective of these problems is to find a policy or strategy, which is a prescription …
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How trial correlations and feedback shape sequential decision-making
To make the best decisions, organisms must flexibly accumulate information, accounting for what is relevant and ignoring what is not. Many decision-making studies focus on sequences of independent trials in which the evidence gathered to make a choice, as well as the resulting actions and feedback, …
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Data-driven robust solution schemes for sequential decision making
… robust and data-efficient methodologies for sequential decision making under uncertainty, motivated by challenges arising in operations research, control, and machine learning. Classical approaches such as sample average approximation—also referred to as empirical risk minimization in the …
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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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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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Integrating Deterministic Planning and Reinforcement Learning for Complex Sequential Decision Making
This thesis presents a novel approach to solving decision-making problems in discrete, stochasticdomains. The method for solving these problems is often dictated by the availability of informationabout how the environment responds to actions taken by the agent. When the agentis given a model of the …
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SEQUENTIAL DECISION MAKING FOR ACTIVE LEARNING AND INFERENCE IN ONLINE SETTINGS
This dissertation focuses on sequential decision making for active learning and inference in online settings. In particular, we consider the settings where the hypothesis space is large and labeled data are expensive. Examples include unusual activities in surveillance feedings, target search among …
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Uncertainty and learning in sequential decision-making : the case of climate policy
… In this dissertation, we construct two-period sequential decision models to represent the choice of a level of emissions abatement over the next decade and another choice for the remainder of this century, both empirical models based on a climate model of intermediate complexity, and analytical …
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Compact parametric models for efficient sequential decision making in high-dimensional, uncertain domains
… in how a single agent can autonomously make sequential decisions in large, high-dimensional, uncertain domains. This thesis presents decision-making algorithms for maximizing the expected sum of future rewards in two types of large, high-dimensional, uncertain situations: when the agent knows …
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