Enhancing Search Efficiency by Using Move Categorization Based on Game Progress in Amazons

  • Yoshinori Higashiuchi
  • Reijer Grimbergen
Part of the Lecture Notes in Computer Science book series (LNCS, volume 4250)


Amazons is a two-player perfect information game with a high branching factor, particularly in the opening. Therefore, improving the efficiency of the search is important for improving the playing strength of an Amazons program. In this paper we propose a new method for improving search in Amazons by using move categories to order moves. The move order is decided by the likelihood of the move actually being selected as the best move. Furthermore, it will be shown that the likelihood of move selection strongly depends upon the stage of the game. Therefore, our method is further refined by adjusting the likelihood of moves according to the progress of the game. Self-play experiments show that using move categories significantly improves the strength of an Amazons program and that combining move categories with game progress is better than using only move categories.


Evaluation Function Search Time Legal Move Good Move Playing Strength 
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.


Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.


  1. 1.
    Avetisyan, H., Lorentz, R.J.: Selective Search in an Amazons Program. In: Schaeffer, J., Müller, M., Björnsson, Y. (eds.) CG 2002. LNCS, vol. 2883, pp. 123–141. Springer, Heidelberg (2003)CrossRefGoogle Scholar
  2. 2.
    Beal, D.: A Generalised Quiescence Search Algorithm. Artificial Intelligence 43, 85–98 (1990)CrossRefGoogle Scholar
  3. 3.
    Buro, M.: ProbCut: An Effective Selective Extension of the Alpha-Beta Algorithm. ICCA Journal 18(2), 71–76 (1995)MathSciNetGoogle Scholar
  4. 4.
    Buro, M.: Experiments with Multi-probcut and a New High-quality Evaluation Function for Othello. In: van den Herik, H.J., Iida, H. (eds.) Games in AI Research, Van Spijk, Venlo, The Netherlands, pp. 77–96 (2000)Google Scholar
  5. 5.
    Hashimoto, T., Kajihara, Y., Sasaki, N., Iida, H., Yoshimura, J.: An Evaluation Function for Amazons. In: van den Herik, H.J., Monien, B. (eds.) 9th Advances in Computer Games (ACG9), Van Spijk, Venlo, The Netherlands, pp. 191–201 (2001)Google Scholar
  6. 6.
    Hashimoto, T., Nagashima, J., Sakuta, M., Uiterwijk, J., Iida, H.: Application of Realization Probability Search for Any Games - a Case Study Using Lines of Action. In: Game Programming Workshop in Japan 2002, Kanagawa, Japan, pp. 81–86 (2002) (in Japanese)Google Scholar
  7. 7.
    Lieberum, J.: An Evaluation Function for the Game of Amazons. In: van den Herik, H.J., Iida, H., Heinz, E.A. (eds.) 10th Advances in Computer Games (ACG10), Many Games, Many Challenges, pp. 299–308. Kluwer Academic Publishers, Boston (2004)Google Scholar
  8. 8.
    Matsubara, H., Iida, H., Grimbergen, R.: Natural Developments in Game Research: From Chess to Shogi to Go. ICCA Journal 19(2), 103–112 (1996)Google Scholar
  9. 9.
    Sasaki, N., Iida, H.: Report on the First Open Computer-Amazon Championship. ICCA Journal 22(1), 41–44 (1999)Google Scholar
  10. 10.
    Soeda, S., Tanaka, T.: Categories for Amazons Moves. In: Game Programming Workshop in Japan 2003, Kanagawa, Japan, pp. 118–121 (2003) (in Japanese)Google Scholar
  11. 11.
    Tsuruoka, Y., Yokoyama, D., Chikayama, T.: Game-tree Search Algorithm Based on Realization Probability. ICGA Journal 25(3), 145–152 (2002)Google Scholar
  12. 12.

Copyright information

© Springer-Verlag Berlin Heidelberg 2006

Authors and Affiliations

  • Yoshinori Higashiuchi
    • 1
  • Reijer Grimbergen
    • 2
  1. 1.Department of Computer ScienceSaga UniversitySagaJapan
  2. 2.Department of InformaticsYamagata UniversityYonezawaJapan

Personalised recommendations