A world championship caliber checkers program
Artificial Intelligence
Using knowledge about the opponent in game-tree search
Using knowledge about the opponent in game-tree search
Probabilistic opponent-model search
Information Sciences: an International Journal - Heuristic Search and Computer Game Playing
Machine Learning
GIB: imperfect information in a computationally challenging game
Journal of Artificial Intelligence Research
Last-branch and speculative pruning algorithms for max
IJCAI'03 Proceedings of the 18th international joint conference on Artificial intelligence
Leaf-value tables for pruning non-zero-sum games
IJCAI'05 Proceedings of the 19th international joint conference on Artificial intelligence
Incorporating opponent models into adversary search
AAAI'96 Proceedings of the thirteenth national conference on Artificial intelligence - Volume 1
Player modeling, search algorithms and strategies in multi-player games
ACG'05 Proceedings of the 11th international conference on Advances in Computer Games
An Analysis of UCT in Multi-player Games
CG '08 Proceedings of the 6th international conference on Computers and Games
Symbolic Classification of General Multi-Player Games
Proceedings of the 2008 conference on ECAI 2008: 18th European Conference on Artificial Intelligence
Prob-Maxn: playing N-player games with opponent models
AAAI'06 proceedings of the 21st national conference on Artificial intelligence - Volume 2
Feature construction for reinforcement learning in hearts
CG'06 Proceedings of the 5th international conference on Computers and games
Model identification in interactive influence diagrams using mutual information
Web Intelligence and Agent Systems
Towards cooperation in adversarial search with confidentiality
HoloMAS'11 Proceedings of the 5th international conference on Industrial applications of holonic and multi-agent systems for manufacturing
Max-Prob: an unbiased rational decision making procedure for multiple-adversary environments
IJCAI'11 Proceedings of the Twenty-Second international joint conference on Artificial Intelligence - Volume Volume One
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A standard assumption of search in two-player games is that the opponent has the same evaluation function or utility for possible game outcomes. While some work has been done to try to better exploit weak opponents, it has only been a minor component of high-performance game playing programs such as Chinook or Deep Blue. However, we demonstrate that in games with more than two players, opponent modeling is a necessary component for ensuring high-quality play against unknown opponents. Thus, we propose a new algorithm, soft-maxn, which can help accommodate differences in opponent styles. Finally, we show an inference mechanism that can be used with soft-maxn to infer the playing style of our opponents.