论文标题
生成的对抗网络房间的生成图形语法地牢中的Zelda传说
Generative Adversarial Network Rooms in Generative Graph Grammar Dungeons for The Legend of Zelda
论文作者
论文摘要
生成的对抗网络（GAN）已经证明了他们在数据中学习模式并产生与他们在包括视频游戏在内的多个领域的训练相似的新示例的能力。但是，甘斯的输出尺寸固定，因此很难为地牢爬行游戏创建任意大小的水平。甘斯在编码语义要求方面也很难使水平变得有趣且可播放。本文结合了一种用图形语法方法生成单个房间的方法，将房间组合到地牢中。 GAN捕获了各个房间的设计原理，但是图形语法将房间组织成一个全球布局，并具有由设计师确定的一系列障碍。 Zelda传说中的房间数据用于训练GAN。该方法通过用户研究来验证，表明Gan Dungeons可以像原始游戏的水平一样有趣，并且仅使用图形语法就产生了级别。但是，Gan Dungeons的房间被认为更为复杂，而Plain Graph Grammar的地牢被认为是最不复杂且具有挑战性的。只有GAN方法才能创造出布局和房间的广泛供应，其中房间遍及训练中看到的范围，以从多个房间中合并设计原理的新作品。
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.
