We present an approach for generating playable games that blend a given set
of games in a desired combination using deep generative latent variable models.
We refer to this approach as latent combinational game design -- latent since
we use learned latent representations to perform blending, combinational since
game blending is a combinational creativity process and game design since the
approach generates novel, playable games. We use Gaussian Mixture Variational
Autoencoders (GMVAEs), which use a mixture of Gaussians to model the VAE latent
space. Through supervised training, each component learns to encode levels from
one game and lets us define new, blended games as linear combinations of these
learned components. This enables generating new games that blend the input
games as well as control the relative proportions of each game in the blend. We
also extend prior work using conditional VAEs to perform blending and compare
against the GMVAE. Our results show that both models can generate playable
blended games that blend the input games in the desired proportions.

通过使用高斯混合变分自编码器 (GMVAEs) 对深度生成潜在变量模型进行建模，并使用线性组合技术生成可玩游戏。通过监督训练，每个组件能够从一个游戏中编码关卡，使我们能够定义新的、混合的游戏作为这些学习组件的线性组合。结果表明，这种方法可以以所需比例混合输入游戏并生成可玩游戏。