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Filtering by: Creator Rossignol, P. A. Remove constraint Creator: Rossignol, P. A. Committee Member Tadepalli, Prasad Remove constraint Committee Member: Tadepalli, Prasad Resource Type Dissertation Remove constraint Resource Type: Dissertation

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  • Incorporating and Learning Behavior Constraints for Sequential Decision Making

    Creator:
    Pinto, Jervis
    Abstract:
    Writing a program that performs well in a complex environment is a challenging task. In such problems, a method of deterministic programming combined with reinforcement learning (RL) can be helpful. However, current systems either force developers to encode knowledge in very specific forms (e.g., state-action features), or assume advanced RL...
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    Dissertation
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    Constraints for Sequential Decision Making Abstract approved: Alan P. Fern Writing a program that performs

  • Effects of individualized examples and personalized contexts in computer-based adaptive teaching of algebra word problems

    Creator:
    Lwo, Lwun-syin
    Abstract:
    The purpose of the present investigation was to develop an adaptive teaching model for an interactive computer-assisted instruction (CAT) program and to evaluate the effectiveness of the implementation of individualized examples or individualized examples in personalized contexts. Translating word problems into equations provided the context for the CAT investigation. Four...
    Resource Type:
    Dissertation

  • Monte Carlo Tree Search with Fixed and Adaptive Abstractions

    Creator:
    Hostetler, Jesse A.
    Abstract:
    Monte Carlo tree search (MCTS) is a class of online planning algorithms for Markov decision processes (MDPs) and related models that has found success in challenging applications. In the online planning approach, the agent makes a decision in the current state by performing a limited forward search over possible futures...
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    Dissertation
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    uncertainty. We consider MDPs of the formM = 〈S,A, P,R, γ〉, where S andA are finite sets of states and

  • Scheduling and Online Planning in Stochastic Diffusion Networks

    Creator:
    Xue, Shan
    Abstract:
    Diffusion processes in networks are common models for many domains, including species colonization, information/idea cascade, disease propagation and fire spreading. In diffusion networks, a diffusion event occurs when a behavior spreads from one node to the other following a probabilistic model, where the behavior could be species, an idea, a...
    Resource Type:
    Dissertation
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    facilitate nesting. Each parcel p has a cost c(p), which denotes the cost of purchasing the land and

  • New learning modes for sequential decision making

    Creator:
    Judah, Kshitij
    Abstract:
    This thesis considers the problem in which a teacher is interested in teaching action policies to computer agents for sequential decision making. The vast majority of policy learning algorithms o er teachers little flexibility in how policies are taught. In particular, one of two learning modes is typically considered: 1)...
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    Dissertation
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    Modes for Sequential Decision Making Abstract approved: Alan P. Fern This thesis considers the

  • Toward computer vision for understanding American football in video

    Creator:
    Hess, Robin W.
    Abstract:
    In this work, I examine the problem of understanding American football in video. In particular, I present several mid-level computer vision algorithms that each accomplish a different sub-task within a larger system for annotating, interpreting, and analyzing collections of American football video. The analysis of football video is useful in...
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    Dissertation
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    Vision for Understanding American Football in Video Abstract approved: Alan P. Fern In this work, I

  • Learning and Improving Policies for Probabilistic Planning Problems

    Creator:
    Issakkimuthu, Murugeswari
    Abstract:
    In this work, we study the problem of learning and improving policies for probabilistic planning problems. In the first part, we train neural network policies for probabilistic planning problems modeled as factored Markov decision problems. The objective is to train problem-specific neural networks via supervised learning to imitate the action...
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    Dissertation
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    produces a probability distribution over actions, P (a|s), which we will denote by the vector P̂ (s). Given

  • Optimization of seasonal irrigation scheduling by genetic algorithms

    Creator:
    Canpolat, Necati
    Abstract:
    In this work, we first introduce a novel approach to the long term irrigation scheduling using Genetic Algorithms (GAs). We explore the effectiveness of GAs in the context of optimizing nonlinear crop models and describe application requirements and implementation of the technique. GAs were found to converge quickly to near-optimal...
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    Dissertation
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    Genetic Algorithms. Abstract approved: John P. Bolte In this work, we first introduce a novel approach

  • Seismic performance of a pile-supported container wharf structures in rockfill

    Creator:
    Kawamata, Yohsuke
    Abstract:
    Recently, the concept of performance based design has become popular for many types of structures, including port facilities under seismic loading. For the case of pile-supported wharves, the level of performance is generally estimated using the displacement capacity of the structure. Therefore, understanding soil-pile interaction is one of the most...
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    Dissertation
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    Pile-supported Container Wharf Structures in Rockfill Abstract approved: Scott A. Ashford

  • Analytical Methods for Improving Causal Inference

    Creator:
    Sinha, Meghamala
    Abstract:
    Causal inference is an important analytical tool to bridge the gap between prediction and decision-making. However, learning a causal network solely from data is a challenging task. In this work, various techniques have been explored for a better and improved causal network learning from data. Firstly, the problem of learning...
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    Dissertation
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    ). A joint distribution p over variables V is called a causal network if and only if it satisfies the