Learning Efficient Classification Procedures and Their Application to Chess End Games

  • J. Ross Quinlan
Part of the Symbolic Computation book series (SYMBOLIC)


A series of experiments dealing with the discovery of efficient classification procedures from large numbers of examples is described, with a case study from the chess end game king-rook versus king-knight. After an outline of the inductive inference machinery used, the paper reports on trials leading to correct and very fast attribute-based rules for the relations lost 2-ply and lost 3-ply. On another tack, a model of the performance of an idealized induction system is developed and its somewhat surprising predictions compared with observed results. The paper ends with a description of preliminary work on the automatic specification of relevant attributes.


Decision Tree Classification Rule Inductive Inference Induction System Game Tree 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.


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  1. Bratko, I. and Mulec, P., “An experiment in automatic learning of diagnostic rules,” Informatika, 1981.Google Scholar
  2. Dietterich, T. G. and Michalski, R. S., “Learning and generalization of characteristic descriptions: evaluation criteria and comparative review of selected methods,” Sixth International Joint Conference on Artificial Intelligence, IJCAI, Tokyo, Japan, pp. 223–231, August 1979.Google Scholar
  3. Hayes-Roth, F. and McDermott, J., “Knowledge acquisition from structural descriptions,” Proceedings of the Fifth International Joint Conference on Artificial Intelligence, IJCAI, Cambridge, Mass., pp. 356–362, August 1977.Google Scholar
  4. Hunt, E. B., Marin, J. and Stone. P. T., Experiments in Induction, Academic Press, New York, 1966.Google Scholar
  5. Kopec, D. and Niblett, T, T., “How hard is the play of the King-Rook King-Knight ending?,” Advances in Computer Chess, volume 2, Clarke, M.R.B. (Ed.), Edinburgh University Press; 1980.Google Scholar
  6. Michalski, R. S., “Pattern recognition as rule-guided inductive inference,” IEEE Transactions on Pattern Analysis and Machine Intelligence, Vol. PAMI-2, No. 4, pp. 349–361, 1980a.MathSciNetCrossRefGoogle Scholar
  7. Mitchell, T. M. M., “An analysis of generalization as a search problem,” Sixth International Joint Conference on Artificial Intelligence, Tokyo, Japan, August 1979.Google Scholar
  8. Quinlan, J. R., “Induction over large data bases”, Technical Report Report HPP-79–14, Heuristic Programming Project, Stanford University, 1979.Google Scholar
  9. Quinlan, J. R., “Discovering rules from large collections of examples: a case study,” Expert Systems in the Micro Electronic Age, Michie, D. (Ed.), Edinburgh University Press, Edinburgh, 1979.Google Scholar
  10. Quinlan, J. R., “Semi-autonomous acquisition of pattern-based knowledge,” Australian Computer Bulletin,April 1980, (also to appear in Michie, D. (ed.), Machine Intelligence 10,Halsted Press, 1982).Google Scholar
  11. Shapiro, A. and Niblett, T, T., “Automatic Induction of classification rules for a chess endgame,” Advances in Computer Chess, volume 3, Clarke, M.R.B. (Ed.), Edinburgh University Press, 1982.Google Scholar
  12. Vere, S. A., “Inductive learning of relational productions,” Pattern-Directed Inference Systems, Waterman, D. A. and Hayes-Roth, F. (Eds.), Academic Press, New York, 1978.Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 1983

Authors and Affiliations

  • J. Ross Quinlan
    • 1
  1. 1.The Rand CorporationUSA

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