This paper presents an approach to the Mario AI Benchmark problem, using the A∗ algorithm for navigation, and an evolutionary process combining routines for the reactiveness of the resulting bot. The Grammatical Evolution system was used to evolve Behaviour Trees, combining both types of routines, while the highly dynamic nature of the environment required specific approaches to deal with over-fitting issues. The results obtained highlight the need for specific algorithms for the different aspects of controlling a bot in a game environment, while Behaviour Trees provided the perfect representation to combine all those algorithms.
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机译：本文介绍了一种使用Mario AI Benchmark问题的方法，该方法使用A ∗ 算法进行导航，并提出了一种结合例程对程序进行反应的进化过程。语法进化系统用于结合两种类型的例程来进化行为树，而环境的高度动态本质要求使用特定方法来解决过度拟合的问题。获得的结果突显了在游戏环境中控制机器人的不同方面需要特定的算法，而“行为树”则提供了将所有这些算法组合在一起的完美表示。
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