@inproceedings{AIIDE27496,
author = {Abraham, Frederic and Stephenson, Matthew},
title = {Utilizing generative adversarial networks for stable structure generation in angry birds},
year = {2023},
isbn = {1-57735-883-X},
publisher = {AAAI Press},
url = {https://doi.org/10.1609/aiide.v19i1.27496},
doi = {10.1609/aiide.v19i1.27496},
abstract = {This paper investigates the suitability of using Generative Adversarial Networks (GANs) to generate stable structures for the physics-based puzzle game Angry Birds. While previous applications of GANs for level generation have been mostly limited to tile-based representations, this paper explores their suitability for creating stable structures made from multiple smaller blocks. This includes a detailed encoding/decoding process for converting between Angry Birds level descriptions and a suitable grid-based representation, as well as utilizing state-of-the-art GAN architectures and training methods to produce new structure designs. Our results show that GANs can be successfully applied to generate a varied range of complex and stable Angry Birds structures.},
booktitle = {Proceedings of the Nineteenth AAAI Conference on Artificial Intelligence and Interactive Digital Entertainment},
articleno = {1},
numpages = {11},
location = {Salt Lake City},
series = {AIIDE '23}
}