Latent Combinational Game Design
This work addresses the problem of automated game design for developers or researchers, but it is incremental as it builds on prior blending techniques with new architectures.
The authors tackled the problem of generating playable games that blend multiple input games in specified combinations using deep generative models, resulting in approaches like GMVAE and CGMVAE that can produce such blended games as demonstrated with platformers and dungeon-based games.
We present latent combinational game design -- an approach for generating playable games that blend a given set of games in a desired combination using deep generative latent variable models. We use Gaussian Mixture Variational Autoencoders (GMVAEs) which model the VAE latent space via a mixture of Gaussian components. Through supervised training, each component encodes levels from one game and lets us define blended games as linear combinations of these components. This enables generating new games that blend the input games as well as controlling the relative proportions of each game in the blend. We also extend prior blending work using conditional VAEs and compare against the GMVAE and additionally introduce a hybrid conditional GMVAE (CGMVAE) architecture which lets us generate whole blended levels and layouts. Results show that these approaches can generate playable games that blend the input games in specified combinations. We use both platformers and dungeon-based games to demonstrate our results.