The Relative History Heuristic

  • Mark H. M. Winands
  • Erik C. D. van der Werf
  • H. Jaap van den Herik
  • Jos W. H. M. Uiterwijk
Part of the Lecture Notes in Computer Science book series (LNCS, volume 3846)

Abstract

In this paper a new method is described for move ordering, called the relative history heuristic. It is a combination of the history heuristic and the butterfly heuristic. Instead of only recording moves which are the best move in a node, we also record the moves which are applied in the search tree. Both scores are taken into account in the relative history heuristic. In this way we favour moves which on average are good over moves which are sometimes best. Experiments in LOA show that our method gives a reduction between 10 and 15 per cent of the number of nodes searched. Preliminary experiments in Go confirm this result. The relative history heuristic seems to be a valuable element in move ordering.

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References

  1. 1.
    Akl, S.G., Newborn, M.M.: The principal continuation and the killer heuristic. In: 1977 ACM Annual Conference Proceedings, pp. 466–473. ACM, Seattle (1977)CrossRefGoogle Scholar
  2. 2.
    Björnsson, Y., Marsland, T.A.: Multi-cut alpha-beta pruning. In: van den Herik, H.J., Iida, H. (eds.) CG 1998. LNCS, vol. 1558, pp. 15–24. Springer, Heidelberg (1999)CrossRefGoogle Scholar
  3. 3.
    Breuker, D.M., Uiterwijk, J.W.H.M., van den Herik, H.J.: Replacement schemes and two-level tables. ICCA Journal 19(3), 175–180 (1996)Google Scholar
  4. 4.
    Campbell, M., Hoane Jr., A.J., Hsu, F.-h.: Deep Blue. Artificial Intelligence 134(1-2), 57–83 (2002)MATHCrossRefGoogle Scholar
  5. 5.
    Davies, J.: Small-board problems. Go World 14–16, 55–56 (1979)Google Scholar
  6. 6.
    Davies, J.: Go in lilliput. Go World 17, 55–56 (1980)Google Scholar
  7. 7.
    Donninger, C.: Null move and deep search: Selective-search heuristics for obtuse chess programs. ICCA Journal 16(3), 137–143 (1993)Google Scholar
  8. 8.
    Hartmann, D.: Butterfly boards. ICCA Journal 11(2-3), 64–71 (1988)Google Scholar
  9. 9.
    Heinz, E.A.: Adaptive null-move pruning. ICCA Journal 22(3), 123–132 (1999)Google Scholar
  10. 10.
    Knuth, D.E., Moore, R.W.: An analysis of alpha-beta pruning. Artificial Intelligence 6(4), 293–326 (1975)MATHCrossRefMathSciNetGoogle Scholar
  11. 11.
    Kocsis, L.: Learning Search Decisions. PhD thesis, Universiteit Maastricht, Maastricht, The Netherlands (2003)Google Scholar
  12. 12.
    Kocsis, L., Uiterwijk, J.W.H.M., van den Herik, H.J.: Move ordering using neural networks. In: Monostori, L., Váncza, J., Ali, M. (eds.) IEA/AIE 2001. LNCS (LNAI), vol. 2070, pp. 45–50. Springer, Heidelberg (2001)CrossRefGoogle Scholar
  13. 13.
    Marsland, T.A., Campbell, M.: Parallel search of strongly ordered game trees. Computing Surveys 14(4), 533–551 (1982)CrossRefGoogle Scholar
  14. 14.
    Sackson, S.: A Gamut of Games. Random House, New York (1969)Google Scholar
  15. 15.
    Schaeffer, J.: The history heuristic. ICCA Journal 6(3), 16–19 (1983)Google Scholar
  16. 16.
    Schaeffer, J.: Experiments in Search and Knowledge. PhD thesis, Department of Computing Science, University of Waterloo, Canada (1986)Google Scholar
  17. 17.
    Schaeffer, J.: The history heuristic and the performance of alpha-beta enhancements. IEEE Transactions on Pattern Analysis and Machine Intelligence 11(11), 1203–1212 (1989)CrossRefGoogle Scholar
  18. 18.
    Schaeffer, J., Plaat, A.: New advances in alpha-beta searching. In: Proceedings of the 1996 ACM 24th Annual Conference on Computer Science, pp. 124–130. ACM Press, New York (1996)CrossRefGoogle Scholar
  19. 19.
    Tsuruoka, Y., Yokoyama, D., Chikayama, T.: Game-tree search algorithm based on realization probability. ICGA Journal 25(3), 132–144 (2002)Google Scholar
  20. 20.
    van der Werf, E.C.D., van den Herik, H.J., Uiterwijk, J.W.H.M.: Solving Go on small boards. ICGA Journal 26(2), 92–107 (2003)Google Scholar
  21. 21.
    Winands, M.H.M., Uiterwijk, J.W.H.M., van den Herik, H.J.: The quad heuristic in Lines of Action. ICGA Journal 24(1), 3–15 (2001)Google Scholar
  22. 22.
    Winands, M.H.M., van den Herik, H.J., Uiterwijk, J.W.H.M., van der Werf, E.C.D.: Enhanced forward pruning. Information Sciences 175(4), 258–272 (2005)CrossRefGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2006

Authors and Affiliations

  • Mark H. M. Winands
    • 1
  • Erik C. D. van der Werf
    • 1
  • H. Jaap van den Herik
    • 1
  • Jos W. H. M. Uiterwijk
    • 1
  1. 1.Department of Computer Science, Institute for Knowledge and Agent TechnologyUniversiteit MaastrichtMaastrichtThe Netherlands

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