Modeling social preferences in multi-player games

Game-tree search algorithms have contributed greatly to the success of computerized players in two-player extensive-form games. In multi-player games there has been less success, partly because of the difficulty of recognizing and reasoning about the inter-player relationships that often develop and change during human game-play. Simplifying assumptions (e.g., assuming each player selfishly aims to maximize its own payoff) have not worked very well in practice. We describe a new algorithm for multi-player games, Socially-oriented Search (SOS), that incorporates ideas from Social Value Orientation theory from social psychology. We provide a theoretical study of the algorithm, and a method for recognizing and reasoning about relationships as they develop and change during a game. Our empirical evaluations of SOS in the strategic board game Quoridor show it to be significantly more effective against players with dynamic interrelationships than the current state-of-the-art algorithms.