Procedural Content Generation through Quality Diversity
This addresses the need for procedural content generation in games, but it is incremental as it builds on existing quality-diversity algorithms and discusses prior applications.
The paper tackles the problem of generating diverse and high-quality content in games by applying quality-diversity algorithms, which produce multiple systematically varying solutions to enable new approaches in creative human-AI interaction and adaptivity.
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.