Generative Adversarial Networks (GANs) have demonstrated their ability to learn patterns in data and produce new exemplars similar to, but different from, their training set in several domains, including video games. However, GANs have a fixed output size, so creating levels of arbitrary size for a dungeon crawling game is difficult. GANs also have trouble encoding semantic requirements that make levels interesting and playable. This paper combines a GAN approach to generating individual rooms with a graph grammar approach to combining rooms into a dungeon. The GAN captures design principles of individual rooms, but the graph grammar organizes rooms into a global layout with a sequence of obstacles determined by a designer. Room data from The Legend of Zelda is used to train the GAN. This approach is validated by a user study, showing that GAN dungeons are as enjoyable to play as a level from the original game, and levels generated with a graph grammar alone. However, GAN dungeons have rooms considered more complex, and plain graph grammar's dungeons are considered least complex and challenging. Only the GAN approach creates an extensive supply of both layouts and rooms, where rooms span across the spectrum of those seen in the training set to new creations merging design principles from multiple rooms.

中文翻译：

---

塞尔达传说生成图文法地下城中的生成对抗网络房间

生成对抗网络 (GAN) 已经证明了它们能够学习数据中的模式并产生与它们在多个领域（包括视频游戏）中的训练集相似但不同的新样本的能力。但是，GAN 具有固定的输出大小，因此很难为地牢爬行游戏创建任意大小的关卡。GAN 也难以编码使关卡变得有趣和可玩的语义要求。本文将生成单个房间的 GAN 方法与将房间组合成地牢的图形语法方法相结合。GAN 捕捉单个房间的设计原则，但图形语法将房间组织成全局布局，其中包含由设计师确定的一系列障碍。塞尔达传说的房间数据用于训练 GAN。此方法已通过用户研究验证，表明 GAN 地牢与原始游戏中的关卡一样令人愉快，并且仅使用图形语法生成关卡。然而，GAN 地牢的房间被认为更复杂，而普通图语法的地牢被认为是最不复杂和最具挑战性的。只有 GAN 方法创建了广泛的布局和房间供应，其中房间跨越了训练集中看到的范围，以及融合了多个房间设计原则的新创作。