Procedural content generation of puzzle games using conditional generative adversarial networks
Artificial Intelligence
2023-06-29 v1 Machine Learning
Abstract
In this article, we present an experimental approach to using parameterized Generative Adversarial Networks (GANs) to produce levels for the puzzle game Lily's Garden. We extract two condition vectors from the real levels in an effort to control the details of the GAN's outputs. While the GANs perform well in approximating the first condition (map shape), they struggle to approximate the second condition (piece distribution). We hypothesize that this might be improved by trying out alternative architectures for both the Generator and Discriminator of the GANs.
Cite
@article{arxiv.2306.15696,
title = {Procedural content generation of puzzle games using conditional generative adversarial networks},
author = {Andreas Hald and Jens Struckmann Hansen and Jeppe Kristensen and Paolo Burelli},
journal= {arXiv preprint arXiv:2306.15696},
year = {2023}
}
Comments
Proceedings of the 15th International Conference on the Foundations of Digital Games 2020