Persistent Link:
http://hdl.handle.net/10150/193649
Title:
Learning to Recognize Agent Activities and Intentions
Author:
Kerr, Wesley
Issue Date:
2010
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
Keywords:
activity recognition; artificial intelligence; classification; data mining; multivariate time series
Degree Name:
Ph.D.
Degree Level:
doctoral
Degree Program:
Computer Science; Graduate College
Degree Grantor:
University of Arizona
Advisor:
Cohen, Paul R.
Committee Chair:
Cohen, Paul R.

Full metadata record

DC FieldValue Language
dc.language.isoenen_US
dc.titleLearning to Recognize Agent Activities and Intentionsen_US
dc.creatorKerr, Wesleyen_US
dc.contributor.authorKerr, Wesleyen_US
dc.date.issued2010en_US
dc.publisherThe University of Arizona.en_US
dc.rightsCopyright © 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.en_US
dc.description.abstractPsychological 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.en_US
dc.typetexten_US
dc.typeElectronic Dissertationen_US
dc.subjectactivity recognitionen_US
dc.subjectartificial intelligenceen_US
dc.subjectclassificationen_US
dc.subjectdata miningen_US
dc.subjectmultivariate time seriesen_US
thesis.degree.namePh.D.en_US
thesis.degree.leveldoctoralen_US
thesis.degree.disciplineComputer Scienceen_US
thesis.degree.disciplineGraduate Collegeen_US
thesis.degree.grantorUniversity of Arizonaen_US
dc.contributor.advisorCohen, Paul R.en_US
dc.contributor.chairCohen, Paul R.en_US
dc.contributor.committeememberAdams, Niall M.en_US
dc.contributor.committeememberFasel, Ianen_US
dc.contributor.committeememberKobourov, Stephen G.en_US
dc.identifier.proquest11232en_US
dc.identifier.oclc752261074en_US
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