| dc.contributor.advisor |
Deb Roy. |
en_US |
| dc.contributor.author |
Orkin, Jeffrey David |
en_US |
| dc.contributor.other |
Massachusetts Institute of Technology. Dept. of Architecture. Program in Media Arts and Sciences. |
en_US |
| dc.date.accessioned |
2008-05-19T16:15:01Z |
|
| dc.date.available |
2008-05-19T16:15:01Z |
|
| dc.date.copyright |
2007 |
en_US |
| dc.date.issued |
2007 |
en_US |
| dc.identifier.uri |
http://hdl.handle.net/1721.1/41757 |
|
| dc.description |
Thesis (S.M.)--Massachusetts Institute of Technology, School of Architecture and Planning, Program in Media Arts and Sciences, 2007. |
en_US |
| dc.description |
Includes bibliographical references (p. 121-123). |
en_US |
| dc.description.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. |
en_US |
| dc.description.provenance |
Made available in DSpace on 2008-05-19T16:15:01Z (GMT). No. of bitstreams: 2
226233576.pdf: 13005314 bytes, checksum: 276ec815781408a2fea2fd2d4159e5b4 (MD5)
226233576-MIT.pdf: 13005122 bytes, checksum: 608044e3fe96161832273fc2b239c2e9 (MD5)
Previous issue date: 2007 |
en |
| dc.description.statementofresponsibility |
by Jeffrey David Orkin. |
en_US |
| dc.format.extent |
123 p. |
en_US |
| dc.language.iso |
eng |
en_US |
| dc.publisher |
Massachusetts Institute of Technology |
en_US |
| dc.rights |
M.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.uri |
http://dspace.mit.edu/handle/1721.1/7582 |
en_US |
| dc.subject |
Architecture. Program in Media Arts and Sciences. |
en_US |
| dc.title |
Learning plan networks in conversational video games |
en_US |
| dc.type |
Thesis |
en_US |
| dc.description.degree |
S.M. |
en_US |
| dc.contributor.department |
Massachusetts Institute of Technology. Dept. of Architecture. Program in Media Arts and Sciences. |
en_US |
| dc.identifier.oclc |
226233576 |
en_US |
