This paper investigates methods for estimating potential territory in the game of Go. We have tested the performance of direct methods known from the literature, which do not require a notion of life and death. Several enhancements are introduced which can improve the performance of the direct methods. New trainable methods are presented for learning to estimate potential territory from examples. The trainable methods can be used in combination with our previously developed method for predicting life and death [25]. Experiments show that all methods are greatly improved by adding knowledge of life and death. © Springer-Verlag Berlin Heidelberg 2006.
CITATION STYLE
Van Der Werf, E. C. D., Van Den Herik, H. J., & Uiterwijk, J. W. H. M. (2006). Learning to estimate potential territory in the game of Go. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (Vol. 3846 LNCS, pp. 81–96). Springer Verlag. https://doi.org/10.1007/11674399_6
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