GP-Gammon: Genetically Programming Backgammon Players

Created by W.Langdon from gp-bibliography.bib Revision:1.7615

@Article{azaria:2005:GPEM,

• author = "Yaniv Azaria and Moshe Sipper",
• title = "{GP-Gammon}: Genetically Programming Backgammon Players",
• 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 = "http://www.cs.bgu.ac.il/~sipper/papabs/gpgammon.pdf",
• URL = "https://rdcu.be/c7iTQ",
• DOI = "doi:10.1007/s10710-005-2990-0",
• size = "18 pages",
• 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 approach.",
• notes = "ECJ",

}

Genetic Programming entries for Yaniv Azaria Moshe Sipper

Citations