论文标题
使用变量自动编码器之间的可控水平混合
Controllable Level Blending between Games using Variational Autoencoders
论文作者
论文摘要
先前的工作探索了现有游戏的混合级别，以创建混合原始游戏属性的新游戏的水平。在本文中，我们使用各种自动编码器（VAE）来改进此类技术。 VAE是人工神经网络，可以学习和使用数据集的潜在表示来产生新的输出。我们在Super Mario Bros.和Kid Icarus的水平数据上训练VAE，使其能够捕获这两款游戏的潜在空间。然后，我们使用此空间来生成级别段，以结合两个游戏的级别属性。此外，通过在潜在空间中应用进化搜索，我们将满足特定约束的水平段进化。我们认为，这些负担能力使基于VAE的方法特别适合共同创造水平的设计，并将其性能与类似的生成模型（如Gan和Vae-Gan）进行比较。
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.
