CHANCEPROBCUT: forward pruning in chance nodes

Authors:
Maarten P. D. Schadd;Mark H. M. Winands;Jos W. H. M. Uiterwijk
Affiliations:
Department of Knowledge Engineering, Faculty of Humanities and Sciences, Maastricht University, Maastricht, The Netherlands;Department of Knowledge Engineering, Faculty of Humanities and Sciences, Maastricht University, Maastricht, The Netherlands;Department of Knowledge Engineering, Faculty of Humanities and Sciences, Maastricht University, Maastricht, The Netherlands
Venue:
CIG'09 Proceedings of the 5th international conference on Computational Intelligence and Games
Year:
2009

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Abstract

This article describes a new, game-independent forward-pruning technique for EXPECTIMAX, called CHANCEPROBCUT. It is the first technique to forward prune in chance nodes. Based on the strong correlation between evaluations obtained from searches at different depths, the technique prunes chance events if the result of the chance node is likely to fall outside the search window. In this article, CHANCEPROBCUT is tested in two games, i.e., Stratego and Dice. Experiments reveal that the technique is able to reduce the search tree significantly without a loss of move quality. Moreover, in both games there is also an increase of playing performance.