A world championship caliber checkers program
Artificial Intelligence
The multi-player version of minimax displays game-tree pathology
Artificial Intelligence
On Pruning Techniques for Multi-Player Games
Proceedings of the Seventeenth National Conference on Artificial Intelligence and Twelfth Conference on Innovative Applications of Artificial Intelligence
Learning and Exploiting Relative Weaknesses of Opponent Agents
Autonomous Agents and Multi-Agent Systems
Prob-Maxn: playing N-player games with opponent models
AAAI'06 proceedings of the 21st national conference on Artificial intelligence - Volume 2
IJCAI'05 Proceedings of the 19th international joint conference on Artificial intelligence
Mixing search strategies for multi-player games
IJCAI'09 Proceedings of the 21st international jont conference on Artifical intelligence
When is it better not to look ahead?
Artificial Intelligence
Towards network games with social preferences
SIROCCO'10 Proceedings of the 17th international conference on Structural Information and Communication Complexity
Current challenges in multi-player game search
CG'04 Proceedings of the 4th international conference on Computers and Games
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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.