Adaptive Game Level Creation through Rank-based Interactive Evolution

Antonios Liapis, Héctor Pérez Martínez, Julian Togelius, Georgios N. Yannakakis

Research output: Conference Article in Proceeding or Book/Report chapterArticle in proceedingsResearchpeer-review


This paper introduces Rank-based Interactive Evolution (RIE) which is an alternative to interactive evolution driven by computational models of user preferences to generate personalized content. In RIE, the computational models are adapted to the preferences of users which, in turn, are used as fitness functions for the optimization of the generated content. The preference models are built via ranking-based preference learning, while the content is generated via evolutionary search. The proposed method is evaluated on the creation of strategy game maps, and its performance is tested using artificial agents. Results suggest that RIE is both faster and more robust than standard interactive evolution and outperforms other state-of-the-art interactive evolution approaches.
Original languageEnglish
Title of host publicationProceedings of the IEEE Conference on Computational Intelligence and Games (CIG)
Number of pages8
PublisherIEEE Computer Society Press
Publication date2013
ISBN (Print)978-1-4673-5308-3
Publication statusPublished - 2013

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