Procedural content generation in video games has a long history. Existing procedural
content generation methods, such as search-based, solver-based, rule-based and grammar …

Georgios N. Yannakakis Julian Togelius Page 1 Artificial Intelligence and Games Georgios N.
Yannakakis Julian Togelius Page 2 Artificial Intelligence and Games Page 3 Georgios N …

This survey explores procedural content generation via machine learning (PCGML), defined
as the generation of game content using machine learning models trained on existing …

Generative Adversarial Networks (GANs) are a machine learning approach capable of
generating novel example outputs across a space of provided training examples. Procedural …

We investigate how reinforcement learning can be used to train level-designing agents. This
represents a new approach to procedural content generation in games, where level design …

One of the main costs of developing a videogame is content creation. Procedural Content
Generation (PCG) can help alleviate that cost by algorithmically generating some of the …

Machine learning advances have afforded an increase in algorithms capable of creating art,
music, stories, games, and more. However, it is not yet well-understood how machine …

Generative Adversarial Networks (GANs) are an emerging form of indirect encoding. The
GAN is trained to induce a latent space on training data, and a real-valued evolutionary …

The design process is often characterized by and realized through the iterative steps of
evaluation and refinement. When the process is based on a single creative domain such as …

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