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GP-Gammon: Genetically Programming Backgammon Players

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Abstract

We apply genetic programming to the evolution of strategies for playing the game of backgammon. We explore two different strategies of learning: using a fixed external opponent as teacher, and letting the individuals play against each other. We conclude that the second approach is better and leads to excellent results: Pitted in a 1000-game tournament against a standard benchmark player—Pubeval—our best evolved program wins 62.4% of the games, the highest result to date. Moreover, several other evolved programs attain win percentages not far behind the champion, evidencing the repeatability of our approach.

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Authors and Affiliations

  1. Department of Computer Science, Ben-Gurion University, Beer-Sheva, 84105, Israel

    Yaniv Azaria & Moshe Sipper

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  1. Yaniv Azaria
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  2. Moshe Sipper
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Correspondence to Yaniv Azaria.

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Azaria, Y., Sipper, M. GP-Gammon: Genetically Programming Backgammon Players. Genet Program Evolvable Mach 6, 283–300 (2005). https://doi.org/10.1007/s10710-005-2990-0

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