Learning social preferences in games

Ya'akov Gal, Avi Pfeffer, Francesca Marzo, Barbara J. Grosz

Research output: Contribution to conferencePaperpeer-review

40 Scopus citations

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.

Original languageEnglish
Pages226-231
Number of pages6
StatePublished - 9 Dec 2004
Externally publishedYes
EventProceedings - Nineteenth National Conference on Artificial Intelligence (AAAI-2004): Sixteenth Innovative Applications of Artificial Intelligence Conference (IAAI-2004) - San Jose, CA, United States
Duration: 25 Jul 200429 Jul 2004

Conference

ConferenceProceedings - Nineteenth National Conference on Artificial Intelligence (AAAI-2004): Sixteenth Innovative Applications of Artificial Intelligence Conference (IAAI-2004)
Country/TerritoryUnited States
CitySan Jose, CA
Period25/07/0429/07/04

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