The Neural MoveMap Heuristic in Chess
- Cite this paper as:
- Kocsis L., Uiterwijk J.W.H.M., Postma E., van den Herik J. (2003) The Neural MoveMap Heuristic in Chess. In: Schaeffer J., Müller M., Björnsson Y. (eds) Computers and Games. CG 2002. Lecture Notes in Computer Science, vol 2883. Springer, Berlin, Heidelberg
The efficiency of alpha-beta search algorithms heavily depends on the order in which the moves are examined. This paper investigates a new move-ordering heuristic in chess, namely the Neural MoveMap (NMM) heuristic. The heuristic uses a neural network to estimate the likelihood of a move being the best in a certain position. The moves considered more likely to be the best are examined first. We develop an enhanced approach to apply the NMM heuristic during the search, by using a weighted combination of the neural-network scores and the history-heuristic scores. Moreover, we analyse the influence of existing game databases and opening theory on the design of the training patterns. The NMM heuristic is tested for middle-game chess positions by the program Crafty. The experimental results indicate that the NMM heuristic outperforms the existing move ordering, especially when a weighted-combination approach is chosen.
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