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Automatic Generation of Search Engines

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Part of the book series: Lecture Notes in Computer Science ((LNTCS,volume 4250))

Abstract

A plethora of enhancements are available to be used together with the αβ search algorithm. There are so many, that their selection and implementation is a non-trivial task, even for the expert. Every domain has its specifics which affect the search tree Even seemingly minute changes to an evaluation function can have an impact on the characteristics of a search tree. In turn, different tree characteristics must be addressed by selecting different enhancements. This paper introduces Pilot, a system for automatically selecting enhancements for αβ search. Pilot generates its own test data and then uses a greedy search to explore the space of possible enhancements. Experiments with multiple domains show differing enhancement selections. Tournament results are presented for two games to demonstrate that automatically generated αβ search performs at least on a par with what is achievable by hand-crafted search engines, but with orders of magnitude less effort in its creation.

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References

  1. Akl, A.G., Newborn, M.M.: The Principle Continuation and the Killer Heuristic. In: ACM Annual Conference, pp. 466–473 (1977)

    Google Scholar 

  2. Björnsson, Y.: Selective Depth-First Game-Tree Search. PhD thesis, University of Alberta (2002)

    Google Scholar 

  3. Buro, M.: Rules of Ataxx (2004), http://www.cs.ualberta.ca/~mburo/ggsa/ax.rules

  4. Buro, M.: Generic Game Server (2005), http://www.cs.ualberta.ca/~mburo/

  5. Buro, M.: ProbCut: An Effective Selective Extension of the αβ Algorithm. ICCA Journal 18(2), 71–76 (1995)

    MathSciNet  Google Scholar 

  6. Cook, D.J., Varnell, R.C.: Adaptive Parallel Iterative Deepening Search. Journal of Artificial Intelligence Research 9, 167–194 (1999)

    MathSciNet  Google Scholar 

  7. Fürnkranz, J.: Machine Learning in Games: A Survey. In: Fürnkranz, J., Kubat, M. (eds.) Machines that Learn to Play Games, ch. 2, pp. 11–59. Nova Science Publishers, Huntington (2001)

    Google Scholar 

  8. Hlynka, M., Schaeffer, J.: Pre-Searching. ICGA Journal 27(4), 203–208 (2004)

    Google Scholar 

  9. Hoffman, P.: Archimedes’ Revenge: The Joys and Perils of Mathematics. W. W. Norton & Company (May 1988)

    Google Scholar 

  10. Knuth, D.E., Moore, R.: An analysis of Alpha-Beta Pruning. Artificial Intelligence 6(4), 293–326 (1975)

    Article  MATH  MathSciNet  Google Scholar 

  11. Plaat, A.: Research Re: Search & Re-search. PhD thesis, Erasmus University, Rotterdam (June 1996)

    Google Scholar 

  12. Romein, J.W., Bal, H.E.: Solving Awari with Parallel Retrograde Analysis. IEEE Computer 36(10), 26–33 (2003)

    Google Scholar 

  13. Schaeffer, J.: The History Heuristic and Alpha-Beta Search Enhancements in Practice. IEEE Transactions on Pattern Analysis and Machine Intelligence PAMI-11(11), 1203–1212 (1989)

    Article  Google Scholar 

  14. Schaeffer, J.: One Jump Ahead. Springer, New York (1997)

    Google Scholar 

  15. Schaeffer, J.: A Gamut of Games. AI Magazine 22(3), 29–46 (2001)

    Google Scholar 

  16. Schaeffer, J., Hlynka, M., Jussila, V.: Temporal Difference Learning Applied to a High Performance Game. In: International Joint Conference on Artificial Intelligence (IJCAI 2001), pp. 529–534 (2001)

    Google Scholar 

  17. Schaeffer, J., Plaat, A.: New Advances in Alpha-Beta Searching. In: ACM Computer Science Conference, pp. 124–130 (1996)

    Google Scholar 

  18. Schaeffer, J., Plaat, A., Junghanns, A.: Unifying Single-Agent and Two-Player Search. Information Sciences 135(3-4), 151–175 (2001)

    Article  MATH  Google Scholar 

  19. Zillions Development Corporation. Zillions of Games – Unlimited Board Games & Puzzles (1998–2005), http://www.zillionsofgames.com/

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© 2006 Springer-Verlag Berlin Heidelberg

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Hlynka, M., Schaeffer, J. (2006). Automatic Generation of Search Engines. In: van den Herik, H.J., Hsu, SC., Hsu, Ts., Donkers, H.H.L.M.(. (eds) Advances in Computer Games. ACG 2005. Lecture Notes in Computer Science, vol 4250. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11922155_3

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  • DOI: https://doi.org/10.1007/11922155_3

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-540-48887-3

  • Online ISBN: 978-3-540-48889-7

  • eBook Packages: Computer ScienceComputer Science (R0)

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