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    Learning to Recognize Agent Activities and Intentions

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    azu_etd_11232_sip1_m.pdf
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    Author
    Kerr, Wesley
    Issue Date
    2010
    Keywords
    activity recognition
    artificial intelligence
    classification
    data mining
    multivariate time series
    Advisor
    Cohen, Paul R.
    Committee Chair
    Cohen, Paul R.
    
    Metadata
    Show full item record
    Publisher
    The University of Arizona.
    Rights
    Copyright © is held by the author. Digital access to this material is made possible by the University Libraries, University of Arizona. Further transmission, reproduction or presentation (such as public display or performance) of protected items is prohibited except with permission of the author.
    Abstract
    Psychological research has demonstrated that subjects shown animations consisting of nothing more than simple geometric shapes perceive the shapes as being alive, having goals and intentions, and even engaging in social activities such as chasing and evading one another. While the subjects could not directly perceive affective state, motor commands, or the beliefs and intentions of the actors in the animations, they still used intentional language to describe the moving shapes. The purpose of this dissertation is to design, develop, and evaluate computational representations and learning algorithms that learn to recognize the behaviors of agents as they perform and execute different activities. These activities take place within simulations, both 2D and 3D. Our goal is to add as little hand-crafted knowledge to the representation as possible and to produce algorithms that perform well over a variety of different activity types. Any patterns found in similar activities should be discovered by the learning algorithm and not by us, the designers. In addition, we demonstrate that if an artificial agent learns about activities through participation, where it has access to its own internal affective state, motor commands, etc., it can then infer the unobservable affective state of other agents.
    Type
    text
    Electronic Dissertation
    Degree Name
    Ph.D.
    Degree Level
    doctoral
    Degree Program
    Computer Science
    Graduate College
    Degree Grantor
    University of Arizona
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