Exploring Level Blending across Platformers via Paths and Affordances
This work addresses the challenge of cross-domain content generation for game designers, though it appears incremental as it builds on existing level blending and domain transfer techniques.
The paper tackles the problem of generating novel game content across multiple platformer domains by introducing a PCGML approach that uses an affordance and path vocabulary to encode data from six games and trains variational autoencoders to capture a latent level space, enabling generation of content with varying domain proportions.
Techniques for procedural content generation via machine learning (PCGML) have been shown to be useful for generating novel game content. While used primarily for producing new content in the style of the game domain used for training, recent works have increasingly started to explore methods for discovering and generating content in novel domains via techniques such as level blending and domain transfer. In this paper, we build on these works and introduce a new PCGML approach for producing novel game content spanning multiple domains. We use a new affordance and path vocabulary to encode data from six different platformer games and train variational autoencoders on this data, enabling us to capture the latent level space spanning all the domains and generate new content with varying proportions of the different domains.