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Authors: Soichiro Takata ; Yuichi Sei ; Yasuyuki Tahara and Akihiko Ohsuga

Affiliation: Department of Informatics, The University of Electro-Communications, Chofu, Tokyo, Japan

Keyword(s): Procedural Content Generation, Generative Adversarial Networks, Deep Learning.

Abstract: The procedural generation of levels in video games has been studied mainly to reduce the burden on producers. In recent years, methods based on deep learning have been attracting attention. In level generations with deep learning, GAN-based methods have achieved some success in tile-based video games, but the preparation of the dataset has been an issue. In this study, we investigate a method to acquire a model that can generate various levels by learning a GAN from only a small amount of data. It was confirmed that a greater variety and lower playability of levels can be generated than with conventional methods by quantitative evaluation of the levels generated by the proposed methods. In addition, the model learned by the proposed method can generate levels that reflect the objectives more strongly than the conventional method by using CMA-ES to search for latent variables.

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Paper citation in several formats:
Takata, S.; Sei, Y.; Tahara, Y. and Ohsuga, A. (2023). Diverse Level Generation for Tile-Based Video Game using Generative Adversarial Networks from Few Samples. In Proceedings of the 15th International Conference on Agents and Artificial Intelligence - Volume 3: ICAART; ISBN 978-989-758-623-1; ISSN 2184-433X, SciTePress, pages 326-333. DOI: 10.5220/0011666200003393

@conference{icaart23,
author={Soichiro Takata. and Yuichi Sei. and Yasuyuki Tahara. and Akihiko Ohsuga.},
title={Diverse Level Generation for Tile-Based Video Game using Generative Adversarial Networks from Few Samples},
booktitle={Proceedings of the 15th International Conference on Agents and Artificial Intelligence - Volume 3: ICAART},
year={2023},
pages={326-333},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0011666200003393},
isbn={978-989-758-623-1},
issn={2184-433X},
}

TY - CONF

JO - Proceedings of the 15th International Conference on Agents and Artificial Intelligence - Volume 3: ICAART
TI - Diverse Level Generation for Tile-Based Video Game using Generative Adversarial Networks from Few Samples
SN - 978-989-758-623-1
IS - 2184-433X
AU - Takata, S.
AU - Sei, Y.
AU - Tahara, Y.
AU - Ohsuga, A.
PY - 2023
SP - 326
EP - 333
DO - 10.5220/0011666200003393
PB - SciTePress