通过地图精英照亮地下城地图、锁门任务和敌人位置的空间
Illuminating the Space of Dungeon Maps, Locked-door Missions and Enemy Placement Through MAP-Elites
摘要：
程序内容生成（PCG）方法是加速游戏开发过程的宝贵工具。此外，PCG也可能作为功能出现在游戏中，例如Moonlighter中的程序地牢生成（PDG）（Digital Sun，2018）。本文介绍了一个扩展版本的进化地牢生成器，通过纳入地图精英人口。我们的地下城等级是离散的，房间里可能有锁门的任务和敌人。我们通过树结构对地下城进行编码，以确保任务的可行性。我们进行了计算和用户反馈实验，以评估我们的PDG方法。他们表明，我们的方法准确地收敛几乎整个MAP精英人口的大多数执行。最后，玩家的反馈表明，他们喜欢生成的水平，他们不能指出一个算法作为一个水平生成器。
英文摘要：
Procedural Content Generation (PCG) methods are valuable tools to speed up the game development process. Moreover, PCG may also present in games as features, such as the procedural dungeon generation (PDG) in Moonlighter (Digital Sun, 2018). This paper introduces an extended version of an evolutionary dungeon generator by incorporating a MAP-Elites population. Our dungeon levels are discretized with rooms that may have locked-door missions and enemies within them. We encoded the dungeons through a tree structure to ensure the feasibility of missions. We performed computational and user feedback experiments to evaluate our PDG approach. They show that our approach accurately converges almost the whole MAP-Elite population for most executions. Finally, players' feedback indicates that they enjoyed the generated levels, and they could not indicate an algorithm as a level generator.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
