  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.
