Learning long-term chess strategies from databases

• 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 

• Baxter, J., Tridgell, A., & Weaver, L. (2000). Learning to play chess using temporal differences. Machine Learning, 40(3), 243–263.

  Article  MATH  Google Scholar 

• 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.

• 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 

• Bratko, I. (2001). Prolog programming for artificial intelligence, 3rd edn. Addison-Wesley Publishing Company.

• Buro, M. (1999). How machines have learned to play Othello. IEEE Intelligent Systems Journal, 14(6), 12–14.

  Google Scholar 

• 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.

• Dvoretsky, M. (2003). Dvoretsky’s endgame manual. Milford, CT: Russell Enterprises.

  Google Scholar 

• 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 

• George, M. & Schaeffer, J. (1990). Chunking for experience. International Computer Chess Association Journal, 13(3), 123–132.

  Google Scholar 

• Morales, E. (1994). Learning patterns for playing strategies. International Computer Chess Association Journal, 17(1), 15–26.

  Google Scholar 

• 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).

• Quinlan, J. R. (1979). Discovering rules by induction from large collections of examples. Expert Systems in the Micro Electronic Age.

• Quinlan, J. R. (1983). Learning efficient classification procedures and their application to chess endgames. Machine Learning: An Artificial Intelligence Approach, 463–482.

• 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.

• 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.

  Article  Google Scholar 

• 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 

• Tesauro, G. (1992). Practical issues in temporal difference learning. Machine Learning, 8(3–4), 257–278.

  MATH  Google Scholar 

• Thompson K. (1986). Retrograde analysis of certain endgames. International Computer Chess Association Journal, 9(3), 131–139.

  Google Scholar 

• Thompson, K. (1996). 6-piece endgames. International Computer Chess Association Journal, 19(4), 215–226.

  Google Scholar 

Download references