- author = "Amit Benbassat and Moshe Sipper",
- title = "{EvoMCTS:} Enhancing {MCTS}-based players through genetic programming",
- booktitle = "IEEE Conference on Computational Intelligence in Games (CIG 2013)",
- year = "2013",
- month = "11-13 " # aug,
- address = "Niagara Falls, Canada",
- keywords = "genetic algorithms, genetic programming, Coevolution, Monte Carlo Tree Search, Upper Confidence Bounds applied to Trees, UCT, CoPlayNum, playoutBranchingFactor",
- isbn13 = "978-1-4673-5311-3",
- ISSN = "2325-4270",
-
URL = "
https://www.moshesipper.com/pubs/evomcts_players.pdf",
-
DOI = "
10.1109/CIG.2013.6633631",
- 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 Dodgem.",
-
notes = "Also known as \cite{6633631}
Department of Computer Science Ben-Gurion University of the Negev Beer-Sheva, Israel",
