Covariance matrix adaptation MAP-Elites for video game level generation

Mengqin Sun; Guifei Jiang; Yuzhi Zhang

doi:10.1117/12.3026544

27 March 2024 Covariance matrix adaptation MAP-Elites for video game level generation

Mengqin Sun, Guifei Jiang, Yuzhi Zhang

Proceedings Volume 13105, International Conference on Computer Graphics, Artificial Intelligence, and Data Processing (ICCAID 2023); 1310533 (2024) https://doi.org/10.1117/12.3026544
Event: 3rd International Conference on Computer Graphics, Artificial Intelligence, and Data Processing (ICCAID 2023), 2023, Qingdao, China

Abstract

How to explore the continuous latent space of Generative Adversarial Networks to extract interesting levels has been a challenge in video game level generation. To take on this challenge, the Covariance Matrix Adaptation MAP-Elites (CMA-ME) algorithm focuses on finding a diverse collection of quality solutions on complex continuous domains. By combining self-adaptation techniques from Covariance Matrix Adaptation Evolution Strategy (CMA-ES) with archiving and mapping techniques, CMA-ME effectively maintains the quality diversity of solutions. In order to obtain various playable levels that are aesthetically similar to human-made ones, this paper defines a new set of behavioral characteristics and fitness functions for the CMA-ME algorithm. It also employs the CMA-ME algorithm to explore the latent space, while simultaneously utilizing the Mixed Integer Linear Programming (MILP) method to ensure the playability of the generated levels. We apply our new method to generate diverse playable levels for several 2D tile-based games: Zelda-v0, Zelda-v1, and Pacman. The experimental results show the high quality of the generated levels compared with the baselines and thus justify the effectiveness of the proposed methods.

(2024) Published by SPIE. Downloading of the abstract is permitted for personal use only.

Citation Download Citation

Mengqin Sun, Guifei Jiang, and Yuzhi Zhang "Covariance matrix adaptation MAP-Elites for video game level generation", Proc. SPIE 13105, International Conference on Computer Graphics, Artificial Intelligence, and Data Processing (ICCAID 2023), 1310533 (27 March 2024); https://doi.org/10.1117/12.3026544

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KEYWORDS

Education and training

Video

Computer programming

Covariance matrices

Gallium nitride

Machine learning

Data modeling

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