Procedural maze level generation with evolutionary cellular automata
Procedural maze level generation with evolutionary cellular automata
复制标题
使用进化细胞自动机生成程序迷宫关卡
DOI：
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发表时间：
2017
期刊：
影响因子：
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通讯作者：
S. Louis
中科院分区：
文献类型：
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作者：
Chad Adams;S. Louis
迷宫运行游戏代表了一种流行的类型的视频游戏和可玩的迷宫的设计提供了一个有趣的研究挑战，在程序内容生成的计算智能的游戏研究。在本文中，我们攻击的问题，创造可玩的迷宫，使用遗传算法进化元胞自动机规则，导致可玩的迷宫。更具体地说，一个固定数量的进化规则的应用程序生成迷宫一样的模式上的细胞自动机网格和区域合并算法，然后生成最终的，可玩的迷宫。由于迷宫路径长度与迷宫可玩性相关，遗传算法搜索导致更长路径长度的元胞自动机规则。从两种类型的细胞自动机和三种不同的适应度函数的路径长度的结果表明，我们的方法的结果在各种有趣的，可玩的迷宫与较长的路径长度和复杂的路径。
Maze running games represent a popular genre of video games and the design of playable mazes provides an interesting research challenge in procedural content generation for computational intelligence research in games. In this paper, we attack the problem of creating playable mazes by using genetic algorithms to evolve cellular automata rules that lead to playable mazes. More specifically, a fixed number of evolved-rule applications generates maze like patterns on a cellular automata grid and a region merging algorithm then generates the final, playable maze. Since maze path lengths correlate with maze playability, the genetic algorithm searches for cellular automata rules that lead to longer path lengths. Results from two types of cellular automata and three different fitness functions of path length show that our approach results in a variety of interesting, playable mazes with longer path lengths and complex paths.