Empowering quality diversity in dungeon design with interactive constrained MAP-Elites

Alberto Alvarez; Steve Dahlskog; Jose Font; Julian Togelius

doi:10.1109/CIG.2019.8848022

Empowering quality diversity in dungeon design with interactive constrained MAP-Elites

Alberto Alvarez, Steve Dahlskog, Jose Font, Julian Togelius

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

Abstract

We propose the use of quality-diversity algorithms for mixed-initiative game content generation. This idea is implemented as a new feature of the Evolutionary Dungeon Designer, a system for mixed-initiative design of the type of levels you typically find in computer role playing games. The feature uses the MAP-Elites algorithm, an illumination algorithm which divides the population into a number of cells depending on their values along several behavioral dimensions. Users can flexibly and dynamically choose relevant dimensions of variation, and incorporate suggestions produced by the algorithm in their map designs. At the same time, any modifications performed by the human feed back into MAP-Elites, and are used to generate further suggestions.

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.2019.8848022
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

Computer Games
Evolutionary Algorithms
Mixed-Initiative Co-Creativity
Procedural Content Generation

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.2019.8848022

Cite this

Empowering quality diversity in dungeon design with interactive constrained MAP-Elites. / Alvarez, Alberto; Dahlskog, Steve; Font, Jose et al.
IEEE Conference on Games 2019, CoG 2019. IEEE Computer Society, 2019. 8848022 (IEEE Conference on Computatonal Intelligence and Games, CIG; Vol. 2019-August).

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

Alvarez, A, Dahlskog, S, Font, J & Togelius, J 2019, Empowering quality diversity in dungeon design with interactive constrained MAP-Elites. in IEEE Conference on Games 2019, CoG 2019., 8848022, 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.2019.8848022

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abstract = "We propose the use of quality-diversity algorithms for mixed-initiative game content generation. This idea is implemented as a new feature of the Evolutionary Dungeon Designer, a system for mixed-initiative design of the type of levels you typically find in computer role playing games. The feature uses the MAP-Elites algorithm, an illumination algorithm which divides the population into a number of cells depending on their values along several behavioral dimensions. Users can flexibly and dynamically choose relevant dimensions of variation, and incorporate suggestions produced by the algorithm in their map designs. At the same time, any modifications performed by the human feed back into MAP-Elites, and are used to generate further suggestions.",

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AB - We propose the use of quality-diversity algorithms for mixed-initiative game content generation. This idea is implemented as a new feature of the Evolutionary Dungeon Designer, a system for mixed-initiative design of the type of levels you typically find in computer role playing games. The feature uses the MAP-Elites algorithm, an illumination algorithm which divides the population into a number of cells depending on their values along several behavioral dimensions. Users can flexibly and dynamically choose relevant dimensions of variation, and incorporate suggestions produced by the algorithm in their map designs. At the same time, any modifications performed by the human feed back into MAP-Elites, and are used to generate further suggestions.

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Empowering quality diversity in dungeon design with interactive constrained MAP-Elites

Abstract

Publication series

Conference

Keywords

ASJC Scopus subject areas

Access to Document

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