Previous work explored blending levels from existing games to create levels
for a new game that mixes properties of the original games. In this paper, we
use Variational Autoencoders (VAEs) for improving upon such techniques. VAEs
are artificial neural networks that learn and use latent representations of
datasets to generate novel outputs. We train a VAE on level data from Super
Mario Bros. and Kid Icarus, enabling it to capture the latent space spanning
both games. We then use this space to generate level segments that combine
properties of levels from both games. Moreover, by applying evolutionary search
in the latent space, we evolve level segments satisfying specific constraints.
We argue that these affordances make the VAE-based approach especially suitable
for co-creative level design and compare its performance with similar
generative models like the GAN and the VAE-GAN.

本研究使用变分自编码器（VAEs）对游戏关卡进行创意设计，通过训练 VAE 于超级马里奥兄弟和战神等游戏的数据，捕捉潜在空间，然后将这些空间用于生成结合了两种游戏特征的新的关卡片段，并通过潜在空间中的进化搜索进行进一步的优化。与类似的生成模型 Gan 和 VAE-GAN 相比，VAE 的方法在协作关卡设计方面表现出特别的优势。