论文标题
生成对抗网络的潜在级别设计空间内的交互式演变和探索
Interactive Evolution and Exploration Within Latent Level-Design Space of Generative Adversarial Networks
论文作者
论文摘要
生成对抗网络（GAN）是间接编码的新兴形式。对GAN进行了训练，可以在训练数据上诱导潜在空间，而实现的进化算法可以搜索该潜在空间。这种潜在变量演化（LVE）最近已应用于游戏水平。但是，客观分数很难捕获吸引玩家的水平功能。因此，本文介绍了一种用于游戏基于瓷砖的级别的工具。该工具还允许直接探索潜在维度，并允许用户播放发现的级别。该工具适用于为超级马里奥兄弟和《塞尔达传说》训练的各种GAN模型，并且很容易推广到其他游戏。一项用户研究表明，进化和潜在的空间探索功能都得到了赞赏，对直接探索略有偏爱，但是结合这些功能使用户可以发现更好的水平。用户反馈还指出了该系统最终如何发展成商业设计工具，并增加了一些增强功能。
Generative Adversarial Networks (GANs) are an emerging form of indirect encoding. The GAN is trained to induce a latent space on training data, and a real-valued evolutionary algorithm can search that latent space. Such Latent Variable Evolution (LVE) has recently been applied to game levels. However, it is hard for objective scores to capture level features that are appealing to players. Therefore, this paper introduces a tool for interactive LVE of tile-based levels for games. The tool also allows for direct exploration of the latent dimensions, and allows users to play discovered levels. The tool works for a variety of GAN models trained for both Super Mario Bros. and The Legend of Zelda, and is easily generalizable to other games. A user study shows that both the evolution and latent space exploration features are appreciated, with a slight preference for direct exploration, but combining these features allows users to discover even better levels. User feedback also indicates how this system could eventually grow into a commercial design tool, with the addition of a few enhancements.
