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
Prob-Maxn: playing N-player games with opponent models
AAAI'06 proceedings of the 21st national conference on Artificial intelligence - Volume 2
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
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Game-tree search algorithms, such as the two-player Minimax algorithm and its multi-player counterpart, Max-n, are a fundamental component in the development of computer programs for playing extensive-form games. The success of these algorithms is limited by the underlying assumptions on which they are built. For example, it is traditionally assumed that deeper search always produces better decisions and also that search procedures can assume all players are selfish and ignore social orientations. Deviations from these assumptions can occur in real games and can affect the success of a traditional search algorithms. The goal of my thesis is to determine when such deviations occur and modify the search procedure to correct the errors that are introduced.