Machine Learning

, Volume 63, Issue 3, pp 329–340 | Cite as

Learning long-term chess strategies from databases

Technical Note

Abstract

We propose an approach to the learning of long-term plans for playing chess endgames. We assume that a computer-generated database for an endgame is available, such as the king and rook vs. king, or king and queen vs. king and rook endgame. For each position in the endgame, the database gives the “value” of the position in terms of the minimum number of moves needed by the stronger side to win given that both sides play optimally. We propose a method for automatically dividing the endgame into stages characterised by different objectives of play. For each stage of such a game plan, a stage-specific evaluation function is induced, to be used by minimax search when playing the endgame. We aim at learning playing strategies that give good insight into the principles of playing specific endgames. Games played by these strategies should resemble human expert’s play in achieving goals and subgoals reliably, but not necessarily as quickly as possible.

Keywords

Machine learning Computer chess Long-term Strategy Chess endgames Chess da-ta-ba-ses 

References

  1. Bain, M. & Srinivasan, A. (1995). Inductive logic programming with large-scale unstructured data. In K. Furukawa, D. Michie, and S. Muggleton (Eds.), Machine Intelligence 14. Oxford: Clarendon Press.Google Scholar
  2. Baxter, J., Tridgell, A., & Weaver, L. (2000). Learning to play chess using temporal differences. Machine Learning, 40(3), 243–263.CrossRefMATHGoogle Scholar
  3. Blake, C. L. & Merz, C. J. (1998). UCI repository of machine learning databases. http://www.ics.uci.edu/mlearn/MLRepository.html, Department of Information and Computer Science, University of California, Irvine, CA.
  4. Bratko, I. (1984). Advice and planning in chess endgames. In A. Elithorn and R. Banerji (Eds.), Artificial and human thinking (pp. 119–130). Amsterdam: North-Holland.Google Scholar
  5. Bratko, I. (2001). Prolog programming for artificial intelligence, 3rd edn. Addison-Wesley Publishing Company.Google Scholar
  6. Buro, M. (1999). How machines have learned to play Othello. IEEE Intelligent Systems Journal, 14(6), 12–14.Google Scholar
  7. Demšar, J., Zupan, B., & Leban, G. (2004). Orange: From experimental machine learning to interactive data mining. http://www.ailab.si/orange (White Paper), Faculty of Computer and Information Science, University of Ljubljana.
  8. Dvoretsky, M. (2003). Dvoretsky’s endgame manual. Milford, CT: Russell Enterprises.Google Scholar
  9. Fürnkranz, J. (2001). Machine learning in games: A survey. In J. Fürnkranz and M. Kubat (Eds.), Machines that learn to play games. New York, NJ: Nova Scientific Publishers.Google Scholar
  10. George, M. & Schaeffer, J. (1990). Chunking for experience. International Computer Chess Association Journal, 13(3), 123–132.Google Scholar
  11. Morales, E. (1994). Learning patterns for playing strategies. International Computer Chess Association Journal, 17(1), 15–26.Google Scholar
  12. Muggleton, S. (1989). DUCE, an oracle based approach to constructive induction. In Proceedings of the Tenth International Joint Conference on Artificial Intelligence. Morgan Kaufmann (pp. 287–292).Google Scholar
  13. Quinlan, J. R. (1979). Discovering rules by induction from large collections of examples. Expert Systems in the Micro Electronic Age.Google Scholar
  14. Quinlan, J. R. (1983). Learning efficient classification procedures and their application to chess endgames. Machine Learning: An Artificial Intelligence Approach, 463–482.Google Scholar
  15. Quinlan, J. R. (1986). Induction of decision trees. In J. W. Shavlik and T. G. Dietterich (eds.), Readings in machine learning. Morgan Kaufmann, Originally published in Machine Learning, 1(1), 81–106.Google Scholar
  16. Samuel, A. L. (1967). Some studies in machine learning using the game of checkers II—recent progress. IBM Journal of Research and Development, 11(6), 601–617.CrossRefGoogle Scholar
  17. Shapiro, A. D. & Niblett, T. (1982) Automatic induction of classification rules for a chess endgame. In M. R. B. Clarke (Ed.), Advances in computer chess 3 (pp. 73–92). Oxford: Pergamon Press.Google Scholar
  18. Tesauro, G. (1992). Practical issues in temporal difference learning. Machine Learning, 8(3–4), 257–278.MATHGoogle Scholar
  19. Thompson K. (1986). Retrograde analysis of certain endgames. International Computer Chess Association Journal, 9(3), 131–139.Google Scholar
  20. Thompson, K. (1996). 6-piece endgames. International Computer Chess Association Journal, 19(4), 215–226.Google Scholar

Copyright information

© Springer Science + Business Media, LLC 2006

Authors and Affiliations

  1. 1.Faculty of Computer and Information ScienceUniversity of LjubljanaLjubljanaSlovenia

Personalised recommendations