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The Neural MoveMap Heuristic in Chess

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Part of the Lecture Notes in Computer Science book series (LNCS,volume 2883)

Abstract

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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© 2003 Springer-Verlag Berlin Heidelberg

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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. https://doi.org/10.1007/978-3-540-40031-8_11

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  • DOI: https://doi.org/10.1007/978-3-540-40031-8_11

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-540-20545-6

  • Online ISBN: 978-3-540-40031-8

  • eBook Packages: Springer Book Archive