Learning plan networks in conversational video games
Citable URI: http://hdl.handle.net/1721.1/41757
| Title: | Learning plan networks in conversational video games |
| Author: | Orkin, Jeffrey David |
| Advisor: | Deb Roy. |
| Department: | Massachusetts Institute of Technology. Dept. of Architecture. Program in Media Arts and Sciences. |
| Abstract: | 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. |
| Description: |
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). |
| URI: | http://hdl.handle.net/1721.1/41757 |
| Issue Date: | 2007 |
| Publisher: | Massachusetts Institute of Technology |
| Keywords: | Architecture. Program in Media Arts and Sciences. |
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| Files | Size | Format |
|---|---|---|
| Preview, non-printable (open to all) | 13.00Mb | application/pdf |
| Full printable version (MIT only) | 13.00Mb | application/pdf |
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