Abstract
We propose an approach for developing efficient search algorithms through genetic programming. Focusing on the game of chess we evolve entire game-tree search algorithms to solve the Mate-In-N problem: find a key move such that even with the best possible counterplays, the opponent cannot avoid being mated in (or before) move N. We show that our evolved search algorithms successfully solve several instances of the Mate-In-N problem, for the hardest ones developing 47% less game-tree nodes than CRAFTY—a state-of-the-art chess engine with a ranking of 2614 points. Improvement is thus not over the basic alpha-beta algorithm, but over a world-class program using all standard enhancements.
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References
Allis, L.V., van der Meulen, M., van den Herik, H.J.: Proof-number search. Artificial Intelligence 66, 91–124 (1994)
Beal, D.F., Smith, M.C.: Multiple probes of transposition tables. ICCA Journal 19(4), 227–233 (1996)
Bourzutschky, M.S., Tamplin, J.A., Haworth, G.M.: Chess endgames: 6-man data and strategy. Theoretical Computer Science 349, 140–157 (2005)
Brave, S.: Evolving recursive programs for tree search. In: Angeline, P.J., Kinnear Jr., K.E. (eds.) Advances in Genetic Programming 2, pp. 203–220. MIT Press, Cambridge (1996)
Campbell, M., Hoane Jr., A.J., Hsu, F.-H.: Deep blue. Artificial Intelligence 134(1–2), 57–83 (2002)
Chabris, C.F., Hearst, E.S.: Visualization, pattern recognition, and forward search: Effects of playing speed and sight of the position on grandmaster chess errors. Cognitive Science 27, 637–648 (2003)
Gross, R., Albrecht, K., Kantschik, W., Banzhaf, W.: Evolving chess playing programs. In: Langdon, W.B., Cantú-Paz, E., Mathias, K., Roy, R., Davis, D., Poli, R., Balakrishnan, K., Honavar, V., Rudolph, G., Wegener, J., Bull, L., Potter, M.A., Schultz, A.C., Miller, J.F., Burke, E., Jonoska, N. (eds.) Proceedings of the Genetic and Evolutionary Computation Conference. GECCO 2002, New York, 9-13 July 2002, pp. 740–747. Morgan Kaufmann Publishers, Seattle (2002)
Hauptman, A., Sipper, M.: Analyzing the intelligence of a genetically programmed chess player. In: Late Breaking Papers at the Genetic and Evolutionary Computation Conference 2005, Washington, DC (June 2005)
Hauptman, A., Sipper, M.: GP-endchess: Using genetic programming to evolve chess endgame players. In: Keijzer, M., Tettamanzi, A.G.B., Collet, P., van Hemert, J.I., Tomassini, M. (eds.) EuroGP 2005. LNCS, vol. 3447, pp. 120–131. Springer, Heidelberg (2005)
Hong, T.-P., Huang, K.-Y., Lin, W.-Y.: Adversarial search by evolutionary computation. Evolutionary Computation 9(3), 371–385 (2001)
Hong, T.-P., Huang, K.-Y., Lin, W.-Y.: Applying genetic algorithms to game search trees. Soft Comput. 6(3-4), 277–283 (2002)
Kaindl, H.: Quiescence search in computer chess (Reprint in Computer Game-Playing: Theory and Practice, Ellis Horwood, Chichester, 1983). ACM SIGART Bulletin 80, 124–131 (1982)
Koza, J.R.: Genetic Programming II: Automatic Discovery of Reusable Programs. MIT Press, Cambridge (May 1994)
Marsland, T.A., Campbell, M.S.: A survey of enhancements to the alpha-beta algorithm. In: Proceedings of the ACM National Conference, November 1981, pp. 109–114. ACM Press, New York (1981)
Newborn, M.: Deep blue’s contribution to AI. Ann. Math. Artif. Intell. 28(1-4), 27–30 (2000)
Polgar, L.: Chess : 5334 Problems, Combinations, and Games. Black Dog and Leventhal Publishers (1995)
Russell, S.J., Norvig, P.: Artificial Intelligence: A Modern Approach. Prentice-Hall, Englewood Cliffs (1995)
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Hauptman, A., Sipper, M. (2007). Evolution of an Efficient Search Algorithm for the Mate-In-N Problem in Chess. In: Ebner, M., O’Neill, M., Ekárt, A., Vanneschi, L., Esparcia-Alcázar, A.I. (eds) Genetic Programming. EuroGP 2007. Lecture Notes in Computer Science, vol 4445. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-71605-1_8
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DOI: https://doi.org/10.1007/978-3-540-71605-1_8
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