Optimising Level Generators for General Video Game AI

Olve Drageset*, Mark H. M. Winands, Raluca D. Gaina, Diego Perez-Liebana

*Corresponding author for this work

Research output: Chapter in Book/Report/Conference proceedingConference article in proceedingAcademicpeer-review

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Procedural Content Generation is an active area of research, with more interest being given recently to methods able to produce interesting content in a general context (without task-specific knowledge). To this extent, we focus on procedural level generators within the General Video Game AI framework (GVGAI). This paper proposes several topics of interest. First, a comparison baseline for GVGAI level generators, which is more flexible and robust than the existing alternatives. Second, a composite fitness evaluation function for levels based on AI playtesting. Third, a new parameterized generator, and a Meta Generator for performing parameter search on such generators are introduced. We compare the Meta Generator against random and constructive generator baselines, using the new fitness function, on 3 GVGAI games: Butterflies, Freeway and The Snowman. The Meta Generator is suggested to perform on par with or better than the baselines, depending on the game. Encouraged by these results, the Meta Generator will be submitted to the 2019 GVGAI Level Generation competition.
Original languageEnglish
Title of host publication 2019 IEEE Conference on Games (CoG)
Number of pages8
ISBN (Print)9781728118840
Publication statusPublished - Aug 2019
EventIEEE Conference on Games (CoG) 2019 - People's Palace, Queen Mary University of London, London, United Kingdom
Duration: 20 Aug 201923 Aug 2019

Publication series

SeriesIEEE Conference on Computational Intelligence and Games


ConferenceIEEE Conference on Games (CoG) 2019
Abbreviated titleCOG
Country/TerritoryUnited Kingdom


  • level generation
  • genetic algorithm

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