A potential model pruning in Monte- Carlo go

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, 5 kinds of pruning strategies were evaluated. The best pruning strategy resulted in an 18% reduction of the computational complexity, and the proper combination of two pruning methods resulted in a 23% 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.