A GA based method for search-space reduction of chess game-tree

Abstract

In this study, a GA (Genetic Algorithm) basesented to reduce the chess game tree space. GA is exploited in some studies and by chess engines in order to: 1) tune the weights of the chess evaluation function or 2) to solve particular problems in chess like finding mate in number of moves. Applying GA for reducing the search space of the chess game tree is a new idea being proposed in this study. A GA-based chess engine is designed and implemented where only the branches of the game tree produced by GA are traversed. Improvements in the basic GA to reduce the problem of GA tactic are evident here. To evaluate the efficiency of this new proposed chess engine, it is matched against an engine where the Alpha-Beta pruning and Min-Max algorithm are applied.

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References

  1. 1.

    Shannon C E (1950) XXII. Programming a computer for playing chess. Philos Mag 41:256–275

    MathSciNet  Article  MATH  Google Scholar 

  2. 2.

    Mandziuk J (2010) Knowledge-free and learning-based methods in intelligent game playing. Springer, Berlin

    Book  MATH  Google Scholar 

  3. 3.

    Tim Jones M (2008) Artificial intelligence: a systems approach. Jone & Bartlett Learning Publication, ISBN-13: 9780763773373

  4. 4.

    Bowden B V (1953) Faster than thought. In: A symposium on digital computing machines. Pitman Publishing

  5. 5.

    Hsu F-H (1999) IBM’s deep blue chess grandmaster chips. IEEE Micro 19:70–81

    Article  Google Scholar 

  6. 6.

    Dehghani H, Babamir SM (2015) Effectiveness analysis of genetic algorithm for chess game tree search. In: The 8th international conference of iranian operations research society, pp 251–253

  7. 7.

    Hong T-P, Huang K-Y, Lin W-Y (2001) Adversarial search by evolutionary computation. Evol Comput 9:371–385

    Article  Google Scholar 

  8. 8.

    David O, van den Herik J, Koppel M, Netanyahu N (2014) Genetic algorithms for evolving computer chess programs. In: IEEE transactions on evolutionary computation

  9. 9.

    Vázquez-Fernández E, Coello C A C, Troncoso F D S (2013) An evolutionary algorithm with a history mechanism for tuning a chess evaluation function. Appl Soft Comput 13:3234–3247

    Article  Google Scholar 

  10. 10.

    Nasreddine H, Poh H S, Kendall G (2006) Using an evolutionary algorithm for the tuning of a chess evaluation function based on a dynamic boundary strategy. In: IEEE conference on cybernetics and intelligent systems, pp 1–6

  11. 11.

    Price K, Storn R (1997) Differential evolution–a simple evolution strategy for fast optimization. Dr. Dobb’s J 22:18–24

    MATH  Google Scholar 

  12. 12.

    Price K, Storn R M, Lampinen J A (2006) Differential evolution: a practical approach to global optimization: Springer Science & Business Media

  13. 13.

    Ronkkonen J, Kukkonen S, Price K V (2005) Real-parameter optimization with differential evolution. In: IEEE congress on evolutionary computation. Edinburgh, pp 506–513

  14. 14.

    Boskovic B, Greiner S, Brest J, Zumer V (2006) A differential evolution for the tuning of a chess evaluation function. In: IEEE congress on evolutionary computation. Vancouver, pp 1851–1856

  15. 15.

    Hauptman A (2005) GP-EndChess: using genetic programming to evolve chess endgame players. In: The 8th European conference on genetic programming. Switzerland, pp 120–131

  16. 16.

    Hauptman A, Sipper M (2007) Evolution of an efficient search algorithm for the mate-in-N problem in chess. In: The 10th European conference on genetic programming, pp 78–89

  17. 17.

    Laws of Chess. Available: http://www.fide.com/component/handbook/?id=124&view=article, Access Date: 29-7-2016

  18. 18.

    Veness J, Bair A (2007) Effective use of transposition table in stochastic game tree search. In: IEEE symposium on computational intelligence and games, pp 112–116

  19. 19.

    Millington I, Funge J (2009) Artificial intelligence for games, 2nd edn. Morgan Kaufmann, Elsevier

  20. 20.

    http://chessprogramming.wikispaces/UCI, Access Date: 20-2-2016

  21. 21.

    http://hgm.nubati.net, Access Date: 20-2-2016

  22. 22.

    Skiena S S (2009) The algorithm design manual. Springer, London

    MATH  Google Scholar 

  23. 23.

    http://chessprogramming.wikispaces/Winglet, Access Date: 20-2-2016

  24. 24.

    https://stockfishchess.org, Access Date: 20-2-2015

  25. 25.

    Hellsten J (2010) Mastering chess strategy. Everyman Chess

  26. 26.

    Chabris C F, Hearst E S (2003) Visualization, pattern recognition, and forward search: effects of playing speed and sight of the position on grandmaster chess errors. Cogn Sci 27:637– 648

    Article  Google Scholar 

  27. 27.

    Wilson F, Alberston B (1999) 303 tricky chess tactics. Cardoza Publishing

  28. 28.

    http://chessprogramming.wikispaces.com/Ufim, Access Date: 29-7-2016

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Correspondence to Seyed Morteza Babamir.

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Dehghani, H., Babamir, S.M. A GA based method for search-space reduction of chess game-tree. Appl Intell 47, 752–768 (2017). https://doi.org/10.1007/s10489-017-0918-z

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Keywords

  • Chess game tree
  • Genetic algorithm
  • Alpha-Beta pruning
  • Min-Max algorithm