EvoMCTS: A Scalable Approach for General Game Learning

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@Article{Benbassat:2014:ieeegames,

• author = "Amit Benbassat and Moshe Sipper",
• journal = "IEEE Transactions on Computational Intelligence and AI in Games",
• title = "{EvoMCTS:} A Scalable Approach for General Game Learning",
• year = "2014",
• volume = "6",
• number = "4",
• pages = "382--394",
• month = dec,
• keywords = "genetic algorithms, genetic programming, STGP, MCTS, Board Games, Monte Carlo Methods, Search",
• DOI = "doi:10.1109/TCIAIG.2014.2306914",
• ISSN = "1943-068X",
• size = "29 pages",
• abstract = "We present the application of genetic programming as a generic game learning approach to zero-sum, deterministic, full knowledge board games by evolving board-state evaluation functions to be used in conjunction with Monte Carlo Tree Search (MCTS). Our method involves evolving board-evaluation functions that are then used to guide the MCTS play out strategy. We examine several variants of Reversi, Dodgem, and Hex using strongly typed genetic programming, explicitly defined introns, and a selective directional crossover method. Our results show a proficiency that surpasses that of baseline handcrafted players using equal and in some cases a greater amount of search, with little domain knowledge and no expert domain knowledge. Moreover, our results exhibit scalability.",
• notes = "Also known as \cite{6744581}",

}

Genetic Programming entries for Amit Benbassat Moshe Sipper

Citations