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 …

Procedural content generation (PCG) refers to the practice of generating game content, such
as levels, quests or characters, algorithmically. Motivated by the need to make games …

Training generally-capable agents with reinforcement learning (RL) remains a significant
challenge. A promising avenue for improving the robustness of RL agents is through the use …

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 …

Abstract Procedural Content Generation (PCG) is a technique to generate complex and
diverse environments in an automated way. However, while generating content with PCG …

In this paper, we review recent deep learning advances in the context of how they have
been applied to play different types of video games such as first-person shooters, arcade …

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

Large Language Models (LLMs) are powerful tools, capable of leveraging their training on
natural language to write stories, generate code, and answer questions. But can they …

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 …

Quality-diversity (QD) algorithms search for a set of good solutions which cover a space as
defined by behavior metrics. This simultaneous focus on quality and diversity with explicit …