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Retrograde Analysis of Patterns versus Metaprogramming

  • T. Cazenave
Part of the Studies in Fuzziness and Soft Computing book series (STUDFUZZ, volume 62)

Abstract

The main objective of this chapter is to present a comparative study of two techniques that automatically generate useful knowledge in games. Retrograde analysis of patterns generates pattern databases, starting with a simple definition of a sub-goal in a game and progressively finding all the pattern of given sizes that fulfill this sub-goal. Metaprogramming is based on similar concepts, but instead of generating fixed size patterns, it generates programs. Programs enable to represent knowledge in a more flexible way. However, they may take more time to use than pattern knowledge. We will describe the application of these two methods to the game of Hex, and compare their behaviors on this game.

Keywords

Winning Strategy Domain Theory Forced Move Pattern Database Partial Deduction 
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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References

  1. [1]
    Anselevich, V. (2000), “The game of Hex: an automatic theorem proving approach to game programming,” AAAI 2000.Google Scholar
  2. [2]
    Cazenave, T. (1993), “Apprentissage de la résolution de problèmes de vie et de mort au jeu de Go,” Rapport du DEA d’Intelligence Artificelle de l’Université Paris 6.Google Scholar
  3. [3]
    Cazenave, T. (1996), Système d’Apprentissage par Auto-Observation. Application au Jeu de Go, Ph.D. thesis, University Paris 6.Google Scholar
  4. [4]
    Cazenave, T. (1998), “Metaprogramming forced moves,” Proceedings ECAI98, Brigthon, U.K., pp. 645–649.Google Scholar
  5. [5]
    Cazenave, T. (1998), “Controlled partial deduction of declarative logic programs,” ACM Computing Surveys, vol. 30, no. 3es.Google Scholar
  6. [6]
    Cazenave, T. (2000), “Generation of patterns with external conditions for the game of Go,” Advances in Computer Games, vol. 9.Google Scholar
  7. [7]
    Culberson, J.C. and Schaeffer, J. (1998), “Pattern databases,” Computational Intelligence, vol. 14, no. 3, pp. 318–334.MathSciNetCrossRefGoogle Scholar
  8. [8]
    Gale, D. (1986), “The game of Hex and the Brouwer fixed-point theorem,” American Mathematical Monthly, pp. 818–827.Google Scholar
  9. [9]
    Ginsberg, M.L. (1996), “Partition Search,” AAAI-96.Google Scholar
  10. [10]
    Korf, R. (1997), “Finding optimal solutions to Rubik’s Cube using pattern databases,” AAAI-97, pp. 700–705.Google Scholar
  11. [11]
    Marsland, T.A. and Björnsson, Y. (2000), “From Minimax to Manhattan,” in van den Herik, H.J. and lida, H. (Eds.), Games in AI Research, Universiteit Maastricht, ISBN 90–621–6416–1, pp. 5 – 17.Google Scholar
  12. [12]
    Pitrat, J. (1998), “Games: the next challenge,” ICCA Journal, vol. 21, no. 3, pp.147–156, September.Google Scholar
  13. [13]
    Thompson, K. (1996), “6-Piece endgames,” ICCA Journal, pp. 215–226, December.Google Scholar
  14. [14]
    van Rijswijck, J. (2000), “Are bees better than fruitflies? Experiments with a Hex playing program,” Canadian Conference on AI.Google Scholar

Copyright information

© Physica-Verlag Heidelberg 2001

Authors and Affiliations

  • T. Cazenave

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