The History Heuristic and Alpha-Beta Search Enhancements in Practice
IEEE Transactions on Pattern Analysis and Machine Intelligence
The Secret of Selective Game Tree Search, When Using Random-Error Evaluations
STACS '02 Proceedings of the 19th Annual Symposium on Theoretical Aspects of Computer Science
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
Multiplayer games: algorithms and approaches
Multiplayer games: algorithms and approaches
The reason for the benefits of minimax search
IJCAI'89 Proceedings of the 11th international joint conference on Artificial intelligence - Volume 1
Pathology on game trees revisited, and an alternative to minimaxing
Artificial Intelligence
Robust game play against unknown opponents
AAMAS '06 Proceedings of the fifth international joint conference on Autonomous agents and multiagent systems
Mixing search strategies for multi-player games
IJCAI'09 Proceedings of the 21st international jont conference on Artifical intelligence
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For a long period of time, two person zero-sum games have been in the focus of researchers of various communities. The efforts were mainly driven by the fascination of special competitions such as Deep Blue vs. Kasparov, and of the beauty of parlor games such as Checkers, Backgammon, Othello, and Go. Multi-player games, however, have been investigated considerably less, and although literature of game theory fills books about equilibrium strategies in such games, practical experiences are rare. Recently, Korf, Sturtevant and a few others started highly interesting research activities. We focused on investigating a four-person chess variant, in order to understand the peculiarities of multi-player games without chance components. In this contribution, we present player models and search algorithms that we tested in the four-player chess world. As a result, we may state that the more successful player models can benefit from more efficient algorithms and speed, because searching more deeply leads to better results. Moreover, we present a meta-strategy, which beats a paranoid α-β player, the best known player in multi-player games.