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Multiobjective Exploration of the StarCraft Map Space
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2010 (English)Conference paper, (Refereed)
Abstract [en]

This paper presents a search-based method for generating maps for the popular real-time strategy (RTS) game StarCraft. We devise a representation of StarCraft maps suitable for evolutionary search, along with a set of fitness functions based on predicted entertainment value of those maps, as derived from theories of player experience. A multiobjective evolutionary algorithm is then used to evolve complete StarCraft maps based on the representation and selected fitness functions. The output of this algorithm is a Pareto front approximation visualizing the tradeoff between the several fitness functions used, and where each point on the front represents a viable map. We argue that this method is useful for both automatic and machine-assisted map generation, and in particular that the Pareto fronts are excellent design support tools for human map designers

Place, publisher, year, edition, pages
Copenhagen: IEEE , 2010.
Keyword [en]
Real-time strategy games, RTS, procedural content generation, evolutionary multiobjective optimization
National Category
Computer Science
Identifiers
URN: urn:nbn:se:bth-7672DOI: 10.1109/ITW.2010.5593346Local ID: oai:bth.se:forskinfo578245C95B39A499C125780600332776ISBN: 978-1-4244-6295-7 (print)OAI: oai:DiVA.org:bth-7672DiVA: diva2:835316
Conference
2010 IEEE Conference on Computational Intelligence and Games (CIG)
Available from: 2012-09-18 Created: 2010-12-27 Last updated: 2017-05-23Bibliographically approved

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fulltext(1520 kB)114 downloads
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Hagelbäck, Johan
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School of Computing
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CiteExportLink to record
Permanent link

Direct link
Cite
Citation style
  • apa
  • harvard1
  • ieee
  • modern-language-association-8th-edition
  • vancouver
  • Other style
More styles
Language
  • de-DE
  • en-GB
  • en-US
  • fi-FI
  • nn-NO
  • nn-NB
  • sv-SE
  • Other locale
More languages
Output format
  • html
  • text
  • asciidoc
  • rtf