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Filtering by: Creator Williams, Jordan Remove constraint Creator: Williams, Jordan Degree Field Computer Science Remove constraint Degree Field: Computer Science License All rights reserved Remove constraint License: All rights reserved

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  • Perceptual thresholds for foot slipping in animated characters

    Creator:
    Strawn, Jordan J.
    Abstract:
    The computer game industry continues to progress toward realistic-looking character motion. However, even in state-of-the-art games, the use of motion capture data in character animation may result in errors such as “foot slipping,” where the feet do not match up with the floor properly during translation. Various algorithms have been...
    Resource Type:
    Honors College Thesis
    Full Text:
    Jordan Strawn Perceptual Thresholds for Foot

  • High-Dimensional Reinforcement Learning with Human Feedback

    Creator:
    Curran, William
    Abstract:
    State-of-the-art personal robots need to perform complex manipulation tasks to be viable in complex scenarios. However, many of these robots, like the PR2, use manipulators with high degrees of freedom. High degrees of freedom are desirable from a functionality standpoint, but make the learning task more difficult by adding a...
    Resource Type:
    Dissertation
    Full Text:
    AN ABSTRACT OF THE DISSERTATION OF William Curran for the

  • Using RUBI to partition agents in air traffic problems with hard constraints and reward shaping

    Creator:
    Curran, William
    Abstract:
    Air traffic flow management over the U.S. airpsace is a difficult problem. Current management approaches lead to hundreds of thousands of hours of delay, costing billions of dollars annually. Weather and airport conditions may instigate this delay, but routing decisions balancing delay with congestion contribute significantly to the propagation of...
    Resource Type:
    Masters Thesis
    Full Text:
    Hard Constraints and Reward Shaping AN ABSTRACT OF THE THESIS OF William Curran for the degree of

  • Algorithmic Problems on Simplicial Complexes

    Creator:
    Maxwell, William
    Resource Type:
    Dissertation
    Full Text:
    AN ABSTRACT OF THE DISSERTATION OF William Maxwell for

  • Mental Models of Mere Mortals with Explanations of Reinforcement Learning

    Creator:
    Anderson, Andrew A.
    Abstract:
    How should reinforcement learning (RL) agents explain themselves to humans not trained in AI? To gain insights into this question, we conducted a 124 participant, four-treatment experiment to compare participants’ mental models of an RL agent in the context of a simple Real-Time Strategy (RTS) game. The four treatments isolated...
    Resource Type:
    Masters Thesis

  • Understanding and improving error-correcting output coding

    Creator:
    Kong, Eun Bae
    Abstract:
    Error-correcting output coding (ECOC) is a method for converting a k-classsupervised learning problem into a large number L of two-class supervised learningproblems and then combining the results of these L evaluations. Previous researchhas shown that ECOC can dramatically improve the classi cation accuracy of supervisedlearning algorithms that learn to classify...
    Resource Type:
    Dissertation

  • Model-based approximation methods for reinforcement learning

    Creator:
    Wang, Xin
    Abstract:
    The thesis focuses on model-based approximation methods for reinforcement learning with large scale applications such as combinatorial optimization problems. First, the thesis proposes two new model-based methods to stablize the value–function approximation for reinforcement learning. The first one is the BFBP algorithm, a batch-like reinforcement learning process which iterates between...
    Resource Type:
    Dissertation
    Full Text:
    (Sutton, 1996). Using relatively small test problems, (Peng & Williams, 1994) showed that nearest

  • Bayesian methods for knowledge transfer and policy search in reinforcement learning

    Creator:
    Wilson, Aaron
    Abstract:
    How can an agent generalize its knowledge to new circumstances? To learn effectively an agent acting in a sequential decision problem must make intelligent action selection choices based on its available knowledge. This dissertation focuses on Bayesian methods of representing learned knowledge and develops novel algorithms that exploit the represented...
    Resource Type:
    Dissertation
    Full Text:
    this approach include REINFORCE (Williams, 1992), and OLPOMDP (Baxter et al., 2001). We will examine

  • A recurrent neural network implementation using the graphics processing unit

    Creator:
    Moore, Christopher
    Abstract:
    We took the back-propagation algorithms of Werbos for recurrent and feed-forward neural networks and implemented them on machines with graphics processing units (GPU). The parallelism of these units gave our implementations a 10 to 100 fold increase in speed. For nets with less than 20 neurons the machine performed faster...
    Resource Type:
    Masters Thesis
    Full Text:
    of holding a sense of short-term memory. Jordan, Elman, and Juergen are all famous for their

  • Learning multiple non-redundant codebooks with word clustering for document classification

    Creator:
    Surve, Akshat Sudhakar
    Abstract:
    The problem of document classification has been widely studied in machine learning and data mining. In document classification, most of the popular algorithms are based on the bag-of-words representation. Due to the high dimensionality of the bag-of-words representation, significant research has been conducted to reduce the dimensionality via different approaches....
    Resource Type:
    Masters Thesis