Learning to Play Connect 4: A Study in Attribute Definition for ID3

  • Brendan W. Baird
  • Ray J. Hickey
Conference paper
Part of the Workshops in Computing book series (WORKSHOPS COMP.)


The use of algorithms such as ID3 to induce decision trees and rule sets requires that a set of attributes or features be defined with which to describe objects to be classified. This problem is considered in an application to the game of Connect 4 where the task is to learn a set of rules with which a program can play to a reasonable standard. The attributes used evaluate the current position of a game from the point of view of both players and therefore, to a limited extent, implement a defensive as well as an offensive strategy. The attributes characterise moves made by the ultimate winners in a series of games played by novice and moderately good players.


Game Playing Data Collection Form Rule Induction Board Position Attribute Definition 
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. Anantharaman, T., Campbell, M., & Hsu, F. (1990). Singular extensions: Adding selectivity to brute force searching. Artificial Intelligence, 43, 99–110.CrossRefGoogle Scholar
  2. Bratko, I. (1990). Prolog programming for artificial intelligence. Wokingham: Addison-Wesley.Google Scholar
  3. Cestnik, B. & Bratko, I. (1991). On estimating probabilities in tree pruning. In Y. Kodratoff (Ed.), Machine learning-EWSL-91 (pp. 138–150). Berlin: Springer-Verlag.CrossRefGoogle Scholar
  4. Clark, P. & Boswell, R. (1991). Rule induction with CN2: Some recent improvements. In Y. Kodratoff (Ed.), Machine learning-EWSL-91 (pp. 151–163). Berlin: Springer-Verlag.CrossRefGoogle Scholar
  5. Epstein, S. L. (1990). Learning plans for competitive domains. In B. W. Porter & R. J. Mooney (Eds.), Proceedings of the seventh international conference on machine learning (pp. 190–197). San Mateo: Morgan Kaufmann.Google Scholar
  6. Matheus, C. J. (1991). The need for constructive induction. In L. A. Birnbaum, & G. C. Collins (Eds.), Proceedings of the eighth international workshop in machine learning (pp. 173–177). San Mateo: Morgan Kaufmann.Google Scholar
  7. Michalski, R. S. (1983). A theory and methodology of inductive learning. Artificial Intelligence, 20, 111–161.CrossRefMathSciNetGoogle Scholar
  8. Mingers, J. (1989). An empirical comparison of pruning methods for decision tree induction. Machine Learning, 4, 227–243.CrossRefGoogle Scholar
  9. Quinlan, J.R. (1986). Induction of decision trees. Machine Learning, 1, 81–106.Google Scholar

Copyright information

© British Computer Society 1993

Authors and Affiliations

  • Brendan W. Baird
    • 1
    • 2
  • Ray J. Hickey
    • 1
  1. 1.Department of Computing ScienceUniversity of Ulster at ColeraineN. Ireland
  2. 2.Department of Computing ScienceUniversity of Ulster, Coleraine, Co.L’DerryN. Ireland

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