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 …

Applied machine learning has rapidly spread throughout the physical sciences. In fact,
machine learning-based data analysis and experimental decision-making have become …

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 …

While evolutionary computation and evolutionary robotics take inspiration from nature, they
have long focused mainly on problems of performance optimization. Yet evolution in nature …

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

Traditional optimization algorithms search for a single global optimum that maximizes (or
minimizes) the objective function. Multimodal optimization algorithms search for the highest …

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 …

This paper surveys research on applying neuroevolution (NE) to games. In neuroevolution,
artificial neural networks are trained through evolutionary algorithms, taking inspiration from …

Quality-Diversity optimization is a new family of optimization algorithms that, instead of
searching for a single optimal solution to solving a task, searches for a large collection of …

Computational creativity has traditionally relied on well-controlled, single-faceted and
established domains such as visual art, narrative and audio. On the other hand, research on …