Utilizing Generative Adversarial Networks for Stable Structure Generation in Angry Birds

• URL: http://arxiv.org/abs/2309.02614v1
• Date: Tue, 5 Sep 2023 23:19:13 GMT
• Title: Utilizing Generative Adversarial Networks for Stable Structure Generation in Angry Birds
• Authors: Frederic Abraham, Matthew Stephenson
• Abstract summary: This paper investigates the suitability of using Generative Adversarial Networks (GANs) to generate stable structures for the physics-based puzzle game Angry Birds. Our results show that GANs can be successfully applied to generate a varied range of complex and stable Angry Birds structures.
• Score: 0.9790236766474201
• License: http://creativecommons.org/licenses/by/4.0/
• 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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