EvoMCTS: Enhancing MCTS-based players through genetic programming

Created by W.Langdon from gp-bibliography.bib Revision:1.3872

  author =       "Amit Benbassat and Moshe Sipper",
  booktitle =    "IEEE Conference on Computational Intelligence in Games
                 (CIG 2013)",
  title =        "{EvoMCTS:} Enhancing MCTS-based players through
                 genetic programming",
  year =         "2013",
  month =        "11-13 " # aug,
  keywords =     "genetic algorithms, genetic programming",
  DOI =          "doi:10.1109/CIG.2013.6633631",
  ISSN =         "2325-4270",
  size =         "8 pages",
  abstract =     "We present EvoMCTS, a genetic programming method for
                 enhancing level of play in games. Our work focuses on
                 the zero-sum, deterministic, perfect-information board
                 game of Reversi. Expanding on our previous work on
                 evolving board-state evaluation functions for
                 alpha-beta search algorithm variants, we now evolve
                 evaluation functions that augment the MTCS algorithm.
                 We use strongly typed genetic programming, explicitly
                 defined introns, and a selective directional crossover
                 method. Our system regularly evolves players that
                 outperform MCTS players that use the same amount of
                 search. Our results prove scalable and EvoMCTS players
                 whose search is increased offline still outperform MCTS
                 counterparts. To demonstrate the generality of our
                 method we apply EvoMCTS successfully to the game of
  notes =        "Also known as \cite{6633631}",

Genetic Programming entries for Amit Benbassat Moshe Sipper