Utilizing Generative Adversarial Networks for Stable Structure Generation in Angry Birds

Frederic Abraham, Matthew Stephenson
9/5/2023
cs.LGcs.AIcs.NE

No official code found. Best implementation: Blaxzter/Utilizing-Generative-Adversarial-Networks-for-Stable-Structure-Generation-in-Angry-Birds (2 stars, last commit 2025-02-18).

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.

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Code Implementations(1)

Blaxzter/Utilizing-Generative-Adversarial-Networks-for-Stable-Structure-Generation-in-Angry-BirdsCommunity100%

Repository for the paper Utilizing Generative Adversarial Networks for Stable Structure Generation in Angry Birds. Included in this repository are: The pretrained GAN models to generate Science Birds structures. An application to test various model architectures and the decoding algorithm.

20Python, Jupyter NotebookJul 29, 20231 years agoNOASSERTION

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Cite this paper

@article{abraham2023utilizing,
  title  = {Utilizing Generative Adversarial Networks for Stable Structure Generation in Angry Birds},
  author = {Frederic Abraham and Matthew Stephenson},
  year   = {2023},
  eprint = {2309.02614},
  archivePrefix = {arXiv},
  url    = {http://arxiv.org/abs/2309.02614v1}
}

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