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Filtering by: Creator Price, David P. Remove constraint Creator: Price, David P. Committee Member Tadepalli, Prasad Remove constraint Committee Member: Tadepalli, Prasad

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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...
    Resource Type:
    Dissertation
    Full Text:
    Constraints for Sequential Decision Making Abstract approved: Alan P. Fern Writing a program that performs

  • Pedestrian distraction : pedestrian behaviors at midblock crossings considering geometric and environmental conditions

    Creator:
    Kautz, Julia Anne
    Abstract:
    Pedestrian distraction at roadway crossings has been correlated with a higher risk of pedestrian-vehicle collisions due to the pedestrian's cognitive, visual, and motor attention being drawn to a wide variety of secondary tasks. This study is different from previous field studies of pedestrian midblock crossings in that the geometric layout...
    Resource Type:
    Masters Thesis
    Full Text:
    : ______________________________________________________ David S. Hurwitz Pedestrian distraction at roadway crossings has been correlated with a

  • Computational Approaches for RNA Structure Prediction with Dynamic Programming and Deep Neural Networks

    Creator:
    Deng, Dezhong
    Abstract:
    Our goal is to build a system to model the RNA sequences that reveals their structural information by using efficient dynamic programming algorithms and deep learning approaches. We aim to 1) achieve linear-time for RNA secondary structure prediction based on existing minimum free energy models; 2) utilize deep neural networks...
    Resource Type:
    Dissertation
    Full Text:
    Abstract approved: David A. Hendrix Our goal is to build a system to model the RNA sequences that reveals

  • Re-understanding Finite-State Representations of Recurrent Policy Networks

    Creator:
    Danesh, Mohamad H.
    Abstract:
    We propose an approach for understanding control policies represented as recurrent neural networks. Recent work has approached this problem by transforming such recurrent policy networks into finite-state machines (FSM) and then analyzing the equivalent minimized FSM. While this led to interesting insights, the minimization process can obscure a deeper understanding...
    Resource Type:
    Masters Thesis
    Full Text:
    -understanding Finite-State Representations of Recurrent Policy Networks Abstract approved: Alan P. Fern We

  • 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...
    Resource Type:
    Dissertation
    Full Text:
    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...
    Resource Type:
    Dissertation
    Full Text:
    . Title: Learning and Improving Policies for Probabilistic Planning Problems Abstract approved: Alan P

  • 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
    Full Text:
    Online Planning in Stochastic Diffusion Networks Abstract approved: Alan P. Fern Diffusion processes

  • OME : A Framework for Running Spatially Explicit System Dynamics Models

    Creator:
    Wingo, Patrick C.
    Abstract:
    The Open Modeling Environment (OME) is a tool developed to address some known shortcomings in ecological System Dynamics (SD) modeling research. OME provides a common set of methods for interacting directly with spatial information, reducing the need for modelers to create their own methods for doing so. The environment is...
    Resource Type:
    Masters Thesis
    Full Text:
    Framework for Running Spatially Explicit System Dynamics Models. Abstract approved: John P. Bolte

  • 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...
    Resource Type:
    Dissertation
    Full Text:
    Genetic Algorithms. Abstract approved: John P. Bolte In this work, we first introduce a novel approach

  • 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)...
    Resource Type:
    Dissertation
    Full Text:
    Modes for Sequential Decision Making Abstract approved: Alan P. Fern This thesis considers the