GP-Gammon: Genetically Programming Backgammon Players

Created by W.Langdon from gp-bibliography.bib Revision:1.4524

  author =       "Yaniv Azaria and Moshe Sipper",
  title =        "GP-Gammon: Genetically Programming Backgammon
  journal =      "Genetic Programming and Evolvable Machines",
  year =         "2005",
  volume =       "6",
  number =       "3",
  pages =        "283--300",
  month =        sep,
  note =         "Published online: 12 August 2005",
  keywords =     "genetic algorithms, genetic programming, backgammon,
                 self-learning, STGP, demes, coevolution",
  ISSN =         "1389-2576",
  URL =          "",
  DOI =          "doi:10.1007/s10710-005-2990-0",
  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 percent 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
  notes =        "ECJ",

Genetic Programming entries for Yaniv Azaria Moshe Sipper