Effect of Potential Model on 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, 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.
KeywordsMonte-Carlo Go potential potential gradient pruning
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- 1.Brügmann, B.: Monte Carlo Go. Technical report, Physics Department, Syracuse University (1993)Google Scholar
- 2.Zobrist, A.L.: A model of visual organization for the game of GO. In: Proceedings of the Spring Joint Computer Conference, AFIPS 1969, May 14–16, pp. 103–112 (Spring 1969)Google Scholar
- 3.Nakamura, K., Syuhei, K.: Analyzing Go Board Patterns Based on Numerical Features. IPSJ Journal (The Information Processing Society of Japan) 43(10), 3021–3029 (2002)Google Scholar
- 4.Yajima, T.: Effect of Stone and Effect of Table. SIG Technical Reports, Surugadai, Kanda, Chiyoda: The Information Processing Society of Japan, pp. 41–46 (2009)Google Scholar
- 5.Burrough, P., McDonnell, R.: Principles of Geographical Information Systems, p. 190. Oxford University Press (1998)Google Scholar