Abstract
Despite the undisputed strength of today’s chess-playing programs, the fact that they have to evaluate millions, or even billions, of different positions per move is unsatisfactory. The amount of “computation” carried out by human players is smaller by orders of magnitudes because they employ specific patterns that help them narrow the search tree. Similar approachs hould in principle be feasible also in computer programs. To draw attenion to this issue, we report our experiments with a program that learns to classify chessboard positions that permit the well-known bishop sacrifice at h7. We discuss some problems pertaining to the collection of training examples, their representation, and pre-classification. Classification accuracies achieved with a decision-tree based classifier are encouraging.
This is a preview of subscription content, log in via an institution to check access.
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
deGroot, A. D. (1965). Thought and Choice in Chess, Mouton, The Hague
Flinter, S. and Keane, M. T. (1995). Using Chunking for the Automatic Generation of Cases in Chess. Proceedings of the 1st International Conference on Case Based Reasoning, ICCBR-95, Springer Verlag
Fürnkranz, J. (1996). Machine Learning in Computer Chess: The Next Generation. International Computer Chess Association Journal, 19, 147–160
Gobet, J. and Jansen, P. (1994). Towards a Chess Program Based on a Model of Human Memory. In H. J. van den Herik et al. (eds.) Advances in Computer Chess, 7, pp. 35–60, University of Limburg
Kubat, M. and Matwin, S. (1997). Addressing the Curse of Imbalanced Training Sets: One-Sided Selection. —em Proceedings of the Fourteenth International Conference ICML’97. July 8–12, 1997, Nashville, Tennessee, pp. 179–186.
Mitchell, T. M. (1996). Machine Learning, McGraw Hill
Quinlan, J. R. (1996). Bagging, Boosting, and C4.5. Proceedings of the Eight Annual Conference on Innovative Applications of Artificial Intelligence, AAAI’96. August 4–8, 1996, Portland, Oregon, pp. 725–730.
Shannon, C. E. (1950). Programming a Computer for Playing Chess. Philosophical Magazine, 41(4), 256–275
Shapiro, A. D. (1987). Structured Induction in Expert Systems. Turing Institute Press, Addison-Wesley
Weill, J.-C. (1994). How Hard is the Correct Coding of an Easy Endgame. In H. J. van den Herik et al. (eds.) Advances in Computer Chess, 7, pp. 163–176, University of Limburg
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2000 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Kubat, M., Žižka, J. (2000). Learning Middle-Game Patterns in Chess: A Case Study. In: Logananthara, R., Palm, G., Ali, M. (eds) Intelligent Problem Solving. Methodologies and Approaches. IEA/AIE 2000. Lecture Notes in Computer Science(), vol 1821. Springer, Berlin, Heidelberg. https://doi.org/10.1007/3-540-45049-1_52
Download citation
DOI: https://doi.org/10.1007/3-540-45049-1_52
Published:
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-67689-8
Online ISBN: 978-3-540-45049-8
eBook Packages: Springer Book Archive