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

Using genetic programming to evolve heuristics for a Monte Carlo Tree Search Ms Pac-Man agent
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DOI：
10.1109/cig.2013.6633639
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发表时间：
2013-10
期刊：
2013 IEEE Conference on Computational Inteligence in Games (CIG)
影响因子：
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通讯作者：
Atif M. Alhejali;S. Lucas
Atif M. Alhejali;S. Lucas
中科院分区：
其他
文献类型：
--
作者：
Atif M. Alhejali;S. Lucas

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吃豆人是游戏人工智能（AI）中最具挑战性的测试平台之一。遗传规划和蒙特卡洛树搜索（MCTS）已经成功地应用于包括吃豆人在内的几个游戏。本文中，我们使用蒙特卡洛树搜索创建一个MS吃豆人玩代理之前，使用遗传规划，以提高其性能，进化一个新的默认策略，以取代在模拟中使用的随机代理。具有进化默认策略的新代理能够比具有随机默认策略的代理在其平均得分上增加18%。
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 18% increase on its average score over the agent with random default policy.