论文标题
通过Map-Elites照亮地牢地图，锁定的任务和敌人的位置的空间
Illuminating the Space of Dungeon Maps, Locked-door Missions and Enemy Placement Through MAP-Elites
论文作者
论文摘要
程序内容生成（PCG）方法是加快游戏开发过程的宝贵工具。此外，PCG也可以作为功能中的游戏中存在，例如MoonLighter中的程序地牢发电（PDG）（Digital Sun，2018年）。本文通过合并地图精英人群来介绍进化地牢发电机的扩展版本。我们的地牢级别通过可能锁定门的任务和敌人的房间离散。我们通过树结构编码了地牢，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，我们的方法准确地融合了大多数执行的整个地图精英人群。最后，玩家的反馈表明他们享受了生成的水平，并且无法将算法表示为水平发电机。
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.
