Search Versus Knowledge Revisited Again

  • Aleksander Sadikov
  • Ivan Bratko
Part of the Lecture Notes in Computer Science book series (LNCS, volume 4630)

Abstract

The questions focusing on diminishing returns for additional search effort have been a burning issue in computer chess. Despite a lot of research in this field, there are still some open questions, e.g., what happens at search depths beyond 12 plies, and what is the effect of the program’s knowledge on diminishing returns? The paper presents an experiment that attempts to answer these questions. The results (a) confirm that diminishing returns in chess exist, and more importantly (b) show that the amount of knowledge a program has influences when diminishing returns will start to manifest themselves.

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References

  1. 1.
    Berliner, H., Goetsch, G., Campbell, M., Ebeling, C.: Measuring the Performance Potential of Chess Programs. Artificial Intelligence 43(1), 7–21 (1990)CrossRefGoogle Scholar
  2. 2.
    Haworth, G.: Self Play: Statistical Significance. ICGA Journal 26(2), 115–118 (2003)Google Scholar
  3. 3.
    Heinz, E.A.: A New Self-Play Experiment in Computer Chess. Technical Report No. 608 (MIT-LCS-TM-608), Laboratory for Computer Science, Massachussetts Institute of Technology, USA (2000)Google Scholar
  4. 4.
    Heinz, E.A.: New Self-Play Results in Computer Chess. In: Marsland, T., Frank, I. (eds.) CG 2001. LNCS, vol. 2063, pp. 267–282. Springer, Heidelberg (2002)Google Scholar
  5. 5.
    Heinz, E.A.: Self-Play Experiments in Computer Chess Revisited. In: van den Herik, H.J., Monien, B. (eds.) 9th Advances in Computer Games (ACG9), pp. 73–91. Department of Computer Science, Universiteit Maastricht, Maastricht, The Netherlands (2001)Google Scholar
  6. 6.
    Heinz, E.A.: Follow-up on Self-play, Deep Search, and Diminishing Returns. ICGA Journal 26(2), 75–80 (2003)MathSciNetGoogle Scholar
  7. 7.
    Junghanns, A., Schaeffer, J.: Search Versus Knowledge in Game-Playing Programs Revisited. In: Proceedings of the 15th International Joint Conference on Artificial Intelligence, pp. 692–697. Morgan Kaufmann, San Francisco (1997)Google Scholar
  8. 8.
    Junghanns, A., Schaeffer, J., Brockington, M., Björnsson, Y., Marsland, T.A.: Diminishing Returns for Additional Search in Chess. In: van den Herik, H.J., Uiterwijk, J.W.H.M. (eds.) 8th Advances in Computer Chess (ACC8), pp. 53–67. Department of Computer Science, University of Maastricht, Maastricht, The Netherlands (1997)Google Scholar
  9. 9.
    Krabbé, T.: Open Chess Diary (2006), http://www.xs4all.nl/~timkr/chess2/diary.htm
  10. 10.
    Michie, D.: A Theory of Advice. Machine Intelligence 8, 151–170 (1977)Google Scholar
  11. 11.
    Sadikov, A.: Propagation of Heuristic Evaluation Errors in Game Graphs. PhD thesis, University of Ljubljana, Faculty of Computer and Information Science (2005)Google Scholar
  12. 12.
    Sadikov, A., Bratko, I., Kononenko, I.: Bias and Pathology in Minimax Search. Theoretical Computer Science 349(2), 268–281 (2005)MATHCrossRefMathSciNetGoogle Scholar
  13. 13.
    Thompson, K.: Retrograde Analysis of Certain Endgames. ICCA Journal 9(3), 131–139 (1986)Google Scholar
  14. 14.
    Thompson, K.: 6-Piece Endgames. ICCA Journal 19(4), 215–226 (1996)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2007

Authors and Affiliations

  • Aleksander Sadikov
    • 1
  • Ivan Bratko
    • 1
  1. 1.Faculty of Computer and Information Science, University of Ljubljana, LjubljanaSlovenia

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