# Procedural Content Generation through Quality Diversity

## TL;DR

Quality-diversity algorithms generate diverse high-quality solutions, offering new opportunities for procedural content creation and adaptive AI in games, distinct from traditional evolutionary methods.

## Contribution

This paper introduces the application of quality-diversity algorithms to procedural content generation and discusses future challenges in this emerging field.

## Key findings

- QD algorithms produce diverse, high-quality solutions.
- Application of QD to game content creation shows promising results.
- Identifies key challenges and future directions for QD in games.

## Abstract

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 metrics sets QD algorithms apart from standard single- and multi-objective evolutionary algorithms, as well as from diversity preservation approaches such as niching. These properties open up new avenues for artificial intelligence in games, in particular for procedural content generation. Creating multiple systematically varying solutions allows new approaches to creative human-AI interaction as well as adaptivity. In the last few years, a handful of applications of QD to procedural content generation and game playing have been proposed; we discuss these and propose challenges for future work.