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dc.contributor.advisorDeb Roy.en_US
dc.contributor.authorOrkin, Jeffrey Daviden_US
dc.contributor.otherMassachusetts Institute of Technology. Dept. of Architecture. Program in Media Arts and Sciences.en_US
dc.date.accessioned2008-05-19T16:15:01Z
dc.date.available2008-05-19T16:15:01Z
dc.date.copyright2007en_US
dc.date.issued2007en_US
dc.identifier.urihttp://hdl.handle.net/1721.1/41757
dc.descriptionThesis (S.M.)--Massachusetts Institute of Technology, School of Architecture and Planning, Program in Media Arts and Sciences, 2007.en_US
dc.descriptionIncludes bibliographical references (p. 121-123).en_US
dc.description.abstractWe look forward to a future where robots collaborate with humans in the home and workplace, and virtual agents collaborate with humans in games and training simulations. A representation of common ground for everyday scenarios is essential for these agents if they are to be effective collaborators and communicators. Effective collaborators can infer a partner's goals and predict future actions. Effective communicators can infer the meaning of utterances based on semantic context. This thesis introduces a computational cognitive model of common ground called a Plan Network. A Plan Network is a statistical model that provides representations of social roles, object affordances, and expected patterns of behavior and language. I describe a methodology for unsupervised learning of a Plan Network using a multiplayer video game, visualization of this network, and evaluation of the learned model with respect to human judgment of typical behavior. Specifically, I describe learning the Restaurant Plan Network from data collected from over 5,000 players of an online game called The Restaurant Game.en_US
dc.description.statementofresponsibilityby Jeffrey David Orkin.en_US
dc.format.extent123 p.en_US
dc.language.isoengen_US
dc.publisherMassachusetts Institute of Technologyen_US
dc.rightsM.I.T. theses are protected by copyright. They may be viewed from this source for any purpose, but reproduction or distribution in any format is prohibited without written permission. See provided URL for inquiries about permission.en_US
dc.rights.urihttp://dspace.mit.edu/handle/1721.1/7582en_US
dc.subjectArchitecture. Program in Media Arts and Sciences.en_US
dc.titleLearning plan networks in conversational video gamesen_US
dc.typeThesisen_US
dc.description.degreeS.M.en_US
dc.contributor.departmentMassachusetts Institute of Technology. Dept. of Architecture. Program in Media Arts and Sciences.en_US
dc.identifier.oclc226233576en_US


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