Abstract
In Hex two players try to connect opposing sides by placing pieces onto a rhombus-shaped board of hexagons. The game has a high strategic complexity and the number of possible board positions is larger than in Chess. There are already some Hex programs of recognizable strength, but which still play on a level below very strong human players. One of their major weaknesses is the time for evaluating a board.
In this work we apply machine learning for the computer player to improve his play by generating an fast evaluation function and lookup procedure for pattern endgame databases. The data structures used are neural networks for the evaluation of a position and limited branching trees to determine if a position can be classified as won or lost.
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Kahl, K., Edelkamp, S., Hildebrand, L. (2007). Learning How to Play Hex. In: Hertzberg, J., Beetz, M., Englert, R. (eds) KI 2007: Advances in Artificial Intelligence. KI 2007. Lecture Notes in Computer Science(), vol 4667. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-74565-5_29
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DOI: https://doi.org/10.1007/978-3-540-74565-5_29
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