GAIG Game AI Research Group @ QMUL

Optimising Level Generators for General Video Game AI


Abstract

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 play-testing. 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.
DOI:10.1109/CIG.2019.8847961

Cite this work

@inproceedings{drageset2019generator,
author= {Olve Drageset and Raluca D. Gaina and Diego Perez-Liebana and Mark H.M. Winands},
title= {{Optimising Level Generators for General Video Game AI}},
year= {2019},
booktitle= {{IEEE Conference on Games (COG)}},
pages= {1--8},
doi= {10.1109/CIG.2019.8847961},
abstract= {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 play-testing. 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.},
}

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