2026-09-19T17:12:24Zhttps://dspace.mit.edu/server/oai/requestoai:dspace.mit.edu:1721.1/417572022-01-13T07:55:03Zcom_1721.1_7582com_1721.1_7581col_1721.1_131023
Deb Roy.
Orkin, Jeffrey David
Massachusetts Institute of Technology. Dept. of Architecture. Program in Media Arts and Sciences.
Program in Media Arts and Sciences (Massachusetts Institute of Technology)
2008-05-19T16:15:01Z
2008-05-19T16:15:01Z
2007
2007
http://hdl.handle.net/1721.1/41757
226233576
Thesis (S.M.)--Massachusetts Institute of Technology, School of Architecture and Planning, Program in Media Arts and Sciences, 2007.
Includes bibliographical references (p. 121-123).
We 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.
by Jeffrey David Orkin.
S.M.
123 p.
eng
Massachusetts Institute of Technology
M.I.T. theses are protected by
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http://dspace.mit.edu/handle/1721.1/7582
Architecture. Program in Media Arts and Sciences.
Learning plan networks in conversational video games
Thesis
application/pdf
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