Effect of Potential Model on Monte-Carlo Go

Pruning the igo Game Tree Using Potential and Potential Gradient
Part of the Advances in Intelligent Systems and Computing book series (AISC, volume 194)


In this study, we tackled the reduction of computational complexity by pruning the igo game tree using the potential model based on the knowledge expression of igo. The potential model considers go stones as potentials. Specific potential distributions on the go board result from each arrangement of the stones on the go board. Pruning, using the potential model, categorizes the legal moves into effective and ineffective moves in accordance with the threshold of the potential. In this experiment, 4 kinds of pruning strategies using the potential and 5 kinds of pruning strategies using the potential gradients were evaluated. The reduction rates differed according to how the potential and potential gradients were set. The best pruning strategy resulted in a 20% reduction of the computational complexity. In this research we have successfully demonstrated pruning using the potential model for reducing computational complexity of the go game.


Monte-Carlo Go potential potential gradient pruning 


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Copyright information

© Springer-Verlag Berlin Heidelberg 2013

Authors and Affiliations

  1. 1.Graduate School of Engineering and ScienceUniversity of the RyukyusOkinawaJapan

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