TY - GEN
T1 - Procedural content generation through quality diversity
AU - Gravina, Daniele
AU - Khalifa, Ahmed
AU - Liapis, Antonios
AU - Togelius, Julian
AU - Yannakakis, Georgios N.
N1 - Funding Information:
This project has received funding from the European Union's Horizon 2020 programme under grant agreement No 787476. Ahmed Khalifa acknowledges the financial support from NSF grant (Award number 1717324 - "RI: Small: General Intelligence through Algorithm Invention and Selection.").
Funding Information:
This project has received funding from the European Union’s Horizon 2020 programme under grant agreement No 787476. Ahmed Khalifa acknowledges the financial support from NSF grant (Award number 1717324 - “RI: Small: General Intelligence through Algorithm Invention and Selection.”).
Publisher Copyright:
© 2019 IEEE.
PY - 2019/8
Y1 - 2019/8
N2 - 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.
AB - 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.
KW - Evolutionary Computation
KW - Expressivity
KW - Procedural Content Generation
KW - Quality Diversity
UR - http://www.scopus.com/inward/record.url?scp=85073098034&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=85073098034&partnerID=8YFLogxK
U2 - 10.1109/CIG.2019.8848053
DO - 10.1109/CIG.2019.8848053
M3 - Conference contribution
AN - SCOPUS:85073098034
T3 - IEEE Conference on Computatonal Intelligence and Games, CIG
BT - IEEE Conference on Games 2019, CoG 2019
PB - IEEE Computer Society
T2 - 2019 IEEE Conference on Games, CoG 2019
Y2 - 20 August 2019 through 23 August 2019
ER -