Generative adversarial networks (GANs) are quickly becoming a ubiquitous approach to
procedurally generating video game levels. While GAN generated levels are stylistically …

In this work, we present TOAD-GAN (Token-based One-shot Arbitrary Dimension Generative
Adversarial Network), a novel Procedural Content Generation (PCG) algorithm that …

Procedural Content Generation (PCG) algorithms enable the automatic generation of
complex and diverse artifacts. However, they don't provide high-level control over the …

Procedural Content Generation via Reinforcement Learning (PCGRL) foregoes the need for
large human-authored data-sets and allows agents to train explicitly on functional …

So far, physical labor has ensured that the quality and quantity of game content match the
needs of the game community. However, due to the exponential growth of gaming …

This work presents T oken-based O ne-shot A rbitrary D imension G enerative A dversarial N
etwork (TOAD-GAN), a novel procedural content generation algorithm that generates token …

Variational autoencoders (VAEs) have been used in prior works for generating and blending
levels from different games. To add controllability to these models, conditional VAEs …

1 ABSTRACT Expressive Range Analysis (ERA), an approach for visualising the output of
Procedural Content Generation (PCG) systems, is widely used within PCG research to …

A key concern for procedural level generation is ensuring that generated levels are in fact
playable. One approach for determining playability is using pathfinding agents to test if …

Procedurally generated game levels should be completable. The representation used for
levels and game mechanics impacts the types of games for which different techniques can …