Neurosymbolic Map Generation with VQ-VAE and WFC

Abstract

We introduce a hybrid neural + symbolic approach to map generation that combines neural discrete representation learning with symbolic constraint solving methods. In application to WarCraft II and Super Metroid map designs, we show how a vocabulary of directly manipulable latent tiles can be inferred from the raw pixels of design training data. Despite working with a very small tile vocabulary, our method is able to express a very large effective set of unique tiles at the level of pixel appearances. This work shows new ways of combining generative methods, resulting in directly controllable generators for domains that are primarily specified only by visual design examples.