EvoMCTS: Enhancing MCTS-based players through genetic programming
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- @InProceedings{Benbassat:2013:CIG,
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author = "Amit Benbassat and Moshe Sipper",
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booktitle = "IEEE Conference on Computational Intelligence in Games
(CIG 2013)",
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title = "{EvoMCTS:} Enhancing MCTS-based players through
genetic programming",
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year = "2013",
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month = "11-13 " # aug,
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keywords = "genetic algorithms, genetic programming",
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DOI = "doi:10.1109/CIG.2013.6633631",
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ISSN = "2325-4270",
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size = "8 pages",
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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.",
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notes = "Also known as \cite{6633631}",
- }
Genetic Programming entries for
Amit Benbassat
Moshe Sipper
Citations