Learning Social Preferences in Games
| Title: | Learning Social Preferences in Games |
| Author: |
Gal, Ya'akov; Marzo, Francesca; Grosz, Barbara; Pfeffer, Avrom
Note: Order does not necessarily reflect citation order of authors. |
| Citation: | Gal, Ya’akov, Avi Pfeffer, Francesca Marzo and Barbara J. Grosz. 2004. Learning social preferences in games. In Proceedings, Nineteenth National Conference on Artificial Intelligence: July 25–29, 2004, San Jose, California, ed. National Conference on Artificial Intelligence, 226-231. Menlo Park, California: AAAI Press. |
| Full Text & Related Files: |
Grosz_LearningSocial.pdf (94.45Kb; PDF) 
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| Abstract: | This paper presents a machine-learning approach to modeling human behavior in one-shot games. It provides a framework for representing and reasoning about the social factors that affect people’s play. The model predicts how a human player is likely to react to different actions of another player, and these predictions are used to determine the best possible strategy for that player. Data collection and evaluation of the model were performed on a negotiation game in which humans played against each other and against computer models playing various strategies. A computer player trained on human data outplayed Nash equilibrium and Nash bargaining computer players as well as humans. It also generalized to play people and game situations it had not seen before. |
| Published Version: | http://www.aaai.org/Library/AAAI/aaai04contents.php |
| Terms of Use: | This article is made available under the terms and conditions applicable to Other Posted Material, as set forth at http://nrs.harvard.edu/urn-3:HUL.InstRepos:dash.current.terms-of-use#LAA |
| Citable link to this page: | http://nrs.harvard.edu/urn-3:HUL.InstRepos:2580262 |
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