Using genetic programming to evolve heuristics for a Monte Carlo Tree Search Ms Pac-Man agent

Created by W.Langdon from gp-bibliography.bib Revision:1.8051

@InProceedings{Alhejali:2013:CIG,

• author = "Atif M. Alhejali and Simon M. Lucas",
• title = "Using genetic programming to evolve heuristics for a Monte Carlo Tree Search {Ms Pac-Man} agent",
• 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, Monte Carlo methods, artificial intelligence, computer games, tree searching, Al, MCTS, Monte Carlo tree search Ms Pac-Man agent, evolved default policy, game artificial intelligence, random agent, random default policy, Equations, Games, Mathematical model, Monte Carlo methods, Sociology, Monte Carlo Tree Search, Pac-Man",
• ISSN = "2325-4270",
• isbn13 = "978-1-4673-5311-3",
• DOI = "doi:10.1109/CIG.2013.6633639",
• abstract = "Ms Pac-Man is one of the most challenging test beds in game artificial intelligence (AI). Genetic programming and Monte Carlo Tree Search (MCTS) have already been successful applied to several games including Pac-Man. In this paper, we use Monte Carlo Tree Search to create a Ms Pac-Man playing agent before using genetic programming to enhance its performance by evolving a new default policy to replace the random agent used in the simulations. The new agent with the evolved default policy was able to achieve an 18percent increase on its average score over the agent with random default policy.",
• notes = "Also known as \cite{6633639}",

}

Genetic Programming entries for Atif M Alhejali Simon M Lucas

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