Procedural content generation through quality diversity

Daniele Gravina; Ahmed Khalifa; Antonios Liapis; Julian Togelius; Georgios N. Yannakakis

doi:10.1109/CIG.2019.8848053

Procedural content generation through quality diversity

Daniele Gravina, Ahmed Khalifa, Antonios Liapis, Julian Togelius, Georgios N. Yannakakis

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

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.

Original language	English (US)
Title of host publication	IEEE Conference on Games 2019, CoG 2019
Publisher	IEEE Computer Society
ISBN (Electronic)	9781728118840
DOIs	https://doi.org/10.1109/CIG.2019.8848053
State	Published - Aug 2019
Event	2019 IEEE Conference on Games, CoG 2019 - London, United Kingdom Duration: Aug 20 2019 → Aug 23 2019

Publication series

Name	IEEE Conference on Computatonal Intelligence and Games, CIG
Volume	2019-August
ISSN (Print)	2325-4270
ISSN (Electronic)	2325-4289

Conference

Conference	2019 IEEE Conference on Games, CoG 2019
Country/Territory	United Kingdom
City	London
Period	8/20/19 → 8/23/19

Keywords

Evolutionary Computation
Expressivity
Procedural Content Generation
Quality Diversity

ASJC Scopus subject areas

Artificial Intelligence
Computer Graphics and Computer-Aided Design
Computer Vision and Pattern Recognition
Human-Computer Interaction
Software

Access to Document

10.1109/CIG.2019.8848053

Cite this

Gravina, D, Khalifa, A, Liapis, A, Togelius, J & Yannakakis, GN 2019, Procedural content generation through quality diversity. in IEEE Conference on Games 2019, CoG 2019., 8848053, IEEE Conference on Computatonal Intelligence and Games, CIG, vol. 2019-August, IEEE Computer Society, 2019 IEEE Conference on Games, CoG 2019, London, United Kingdom, 8/20/19. https://doi.org/10.1109/CIG.2019.8848053

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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.",

keywords = "Evolutionary Computation, Expressivity, Procedural Content Generation, Quality Diversity",

author = "Daniele Gravina and Ahmed Khalifa and Antonios Liapis and Julian Togelius and Yannakakis, {Georgios N.}",

note = "Publisher Copyright: {\textcopyright} 2019 IEEE.; 2019 IEEE Conference on Games, CoG 2019 ; Conference date: 20-08-2019 Through 23-08-2019",

year = "2019",

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doi = "10.1109/CIG.2019.8848053",

language = "English (US)",

series = "IEEE Conference on Computatonal Intelligence and Games, CIG",

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AU - Togelius, Julian

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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.

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Procedural content generation through quality diversity

Abstract

Publication series

Conference

Keywords

ASJC Scopus subject areas

Access to Document

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