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.

本文介绍了一种通过将 MAP-Elites population 纳入演化式地下城生成器的扩展版本来生成具有安全任务和敌人的离散化房间的地下城的方法，并通过计算和用户反馈实验来评估其 Procedural Dungeon Generation 的方法。结果表明该方法能准确地收敛，反馈也表明玩家们喜欢这些生成的关卡，并且不能判断算法是否为级别生成器。