Controllable Level Blending between Games using Variational Autoencoders

Abstract

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.

6 pages, 11 figures, Sixth Experimental AI in Games Workshop at AIIDE

References in corpus (3)

• Auto-Encoding Variational Bayes
• The VGLC: The Video Game Level Corpus
• Explainable PCGML via Game Design Patterns

Cited by in corpus (6)

• Deep Learning for Procedural Content Generation
• Procedural content generation of puzzle games using conditional generative adversarial networks
• Generating Lode Runner Levels by Learning Player Paths with LSTMs
• Multi-Domain Level Generation and Blending with Sketches via Example-Driven BSP and Variational Autoencoders
• Conditional Level Generation and Game Blending
• Tile Embedding: A General Representation for Procedural Level Generation via Machine Learning