Multiobjective exploration of the StarCraft map space

Julian Togelius; Mike Preuss; Nicola Beume; Simon Wessing; Johan Hagelbäck; Georgios N. Yannakakis

doi:10.1109/ITW.2010.5593346

Multiobjective exploration of the StarCraft map space

Julian Togelius, Mike Preuss, Nicola Beume, Simon Wessing, Johan Hagelbäck, Georgios N. Yannakakis

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

Abstract

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.

Original language	English (US)
Title of host publication	Proceedings of the 2010 IEEE Conference on Computational Intelligence and Games, CIG2010
Pages	265-272
Number of pages	8
DOIs	https://doi.org/10.1109/ITW.2010.5593346
State	Published - 2010
Event	2010 IEEE Conference on Computational Intelligence and Games, CIG2010 - Copenhagen, Denmark Duration: Aug 18 2010 → Aug 21 2010

Publication series

Name	Proceedings of the 2010 IEEE Conference on Computational Intelligence and Games, CIG2010

Other

Other	2010 IEEE Conference on Computational Intelligence and Games, CIG2010
Country/Territory	Denmark
City	Copenhagen
Period	8/18/10 → 8/21/10

Keywords

RTS
Real-time strategy games
evolutionary multiobjective optimization
procedural content generation

ASJC Scopus subject areas

Artificial Intelligence
Computer Graphics and Computer-Aided Design
Software

Access to Document

10.1109/ITW.2010.5593346

Cite this

Togelius, J., Preuss, M., Beume, N., Wessing, S., Hagelbäck, J., & Yannakakis, G. N. (2010). Multiobjective exploration of the StarCraft map space. In Proceedings of the 2010 IEEE Conference on Computational Intelligence and Games, CIG2010 (pp. 265-272). Article 5593346 (Proceedings of the 2010 IEEE Conference on Computational Intelligence and Games, CIG2010). https://doi.org/10.1109/ITW.2010.5593346

Multiobjective exploration of the StarCraft map space. / Togelius, Julian; Preuss, Mike; Beume, Nicola et al.
Proceedings of the 2010 IEEE Conference on Computational Intelligence and Games, CIG2010. 2010. p. 265-272 5593346 (Proceedings of the 2010 IEEE Conference on Computational Intelligence and Games, CIG2010).

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

Togelius, J, Preuss, M, Beume, N, Wessing, S, Hagelbäck, J & Yannakakis, GN 2010, Multiobjective exploration of the StarCraft map space. in Proceedings of the 2010 IEEE Conference on Computational Intelligence and Games, CIG2010., 5593346, Proceedings of the 2010 IEEE Conference on Computational Intelligence and Games, CIG2010, pp. 265-272, 2010 IEEE Conference on Computational Intelligence and Games, CIG2010, Copenhagen, Denmark, 8/18/10. https://doi.org/10.1109/ITW.2010.5593346

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AB - 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.

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Multiobjective exploration of the StarCraft map space

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Keywords

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