Abstract
Constructing evaluation functions with high accuracy is one of the critical factors in computer game players. This construction is usually done by hand, and deep knowledge of the game and much time to tune them are needed for the construction. To avoid these difficulties, automatic construction of the functions is useful. In this paper, we propose a new method to generate features for evaluation functions automatically based on game records. Evaluation features are built on simple features based on their frequency and mutual information. As an evaluation, we constructed evaluation functions for mate problems in shogi. The evaluation function automatically generated with several thousand evaluation features showed the accuracy of 74% in classifying positions into mate and non-mate.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Preview
Unable to display preview. Download preview PDF.
References
Tesauro, G.: TD-Gammon, a self-teaching backgammon program, achieves master-level play. Neural Comput. 6(2), 215–219 (1994)
Buro, M.: From simple features to sophisticated evaluation functions. In: van den Herik, H.J., Iida, H. (eds.) CG 1998. LNCS, vol. 1558, pp. 126–145. Springer, Heidelberg (1999)
Kaneko, T., Yamaguchi, K., Kawai, S.: Automated Identification of Patterns in Evaluation Functions. In: van den Herik, H.J., Iida, H., Heinz, E.A. (eds.) Advances in Computer Games, vol. 10, pp. 279–298. Kluwer Academic Publishers, Dordrecht (2004)
Fleuret, F.: Fast Binary Feature Selection with Conditional Mutual Information. JMLR 5, 1531–1555 (2004)
Baxter, J., Tridgell, A., Weaver, L.: Learning to Play Chess Using Temporal Differences. Machine Learning 40(3), 243–263 (2000)
Gomboc, D., Marsland, T.A., Buro, M.: Evaluation fuction tuning via ordinal correlation. In: van den Herik, Iida, Heinz (eds.) Advances in Computer Games, pp. 1–18. Kluwer Academic Publishers, Dordrecht (2003)
Fayyad, U.M., Keki, B.: Multi-Interval Discretization of Continuous-Valued Attributes for Classification Learning. In: IJCAI 1993, pp. 1022–1027 (1993)
Uno, T., Kiyomi, M., Arimura, H.: Lcm ver. 3: Collaboration of array, bitmap and prefix tree for frequent itemset mining. In: Proc. of the 1st international workshop on open source data mining, New York, USA, pp. 77–86. ACM Press, New York (2005)
Sakuta, M., Iida, H.: And/or-tree search algorithms in shogi mating search. ICGA Journal 24(4), 218–229 (2001)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2006 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Miwa, M., Yokoyama, D., Chikayama, T. (2006). Automatic Construction of Static Evaluation Functions for Computer Game Players. In: Todorovski, L., Lavrač, N., Jantke, K.P. (eds) Discovery Science. DS 2006. Lecture Notes in Computer Science(), vol 4265. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11893318_37
Download citation
DOI: https://doi.org/10.1007/11893318_37
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-46491-4
Online ISBN: 978-3-540-46493-8
eBook Packages: Computer ScienceComputer Science (R0)