Automatic generation of level maps with the do what's possible representation

Daniel Ashlock, Christoph Salge

Research output: Chapter in Book/Report/Conference proceedingConference contribution

1 Citation (Scopus)
17 Downloads (Pure)

Abstract

Automatic generation of level maps is a popular form of automatic content generation. In this study, a recently developed technique employing the do what's possible representation is used to create open-ended level maps. Generation of the map can continue indefinitely, yielding a highly scalable representation. A parameter study is performed to find good parameters for the evolutionary algorithm used to locate high quality map generators. Variations on the technique are presented, demonstrating its versatility, and an algorithmic variant is given that both improves performance and changes the character of maps located. The ability of the map to adapt to different regions where the map is permitted to occupy space are also tested.
Original languageEnglish
Title of host publicationIEEE Conference on Games 2019, CoG 2019
PublisherIEEE Computer Society
Number of pages8
ISBN (Electronic)9781728118840
DOIs
Publication statusPublished - 26 Sept 2019
Event2019 IEEE Conference on Games, CoG 2019 - London, United Kingdom
Duration: 20 Aug 201923 Aug 2019

Publication series

NameIEEE Conference on Computatonal Intelligence and Games, CIG
Volume2019-August
ISSN (Print)2325-4270
ISSN (Electronic)2325-4289

Conference

Conference2019 IEEE Conference on Games, CoG 2019
Country/TerritoryUnited Kingdom
CityLondon
Period20/08/1923/08/19

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