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Learning to Estimate Potential Territory in the Game of Go

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Computers and Games (CG 2004)

Part of the book series: Lecture Notes in Computer Science ((LNTCS,volume 3846))

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Abstract

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.

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Authors and Affiliations

  1. Department of Computer Science, Institute for Knowlegde and Agent Technology, Universiteit Maastricht, Maastricht, The Netherlands

    Erik C. D. van der Werf, H. Jaap van den Herik & Jos W. H. M. Uiterwijk

Authors
  1. Erik C. D. van der Werf
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  2. H. Jaap van den Herik
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  3. Jos W. H. M. Uiterwijk
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Editor information

Editors and Affiliations

  1. Games and AI Group, MICC, Faculty of Humanities and Sciences, Universiteit Maastricht, Maastricht, The Netherlands

    H. Jaap van den Herik

  2. School of Computer Science, Reykjavík University, Reykjavík, Iceland

    Yngvi Björnsson

  3. Center for Automation Research, University of Maryland, College Park, MD 20742, USA

    Nathan S. Netanyahu

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© 2006 Springer-Verlag Berlin Heidelberg

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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: van den Herik, H.J., Björnsson, Y., Netanyahu, N.S. (eds) Computers and Games. CG 2004. Lecture Notes in Computer Science, vol 3846. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11674399_6

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  • DOI: https://doi.org/10.1007/11674399_6

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-540-32488-1

  • Online ISBN: 978-3-540-32489-8

  • eBook Packages: Computer ScienceComputer Science (R0)

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