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.

翻译：本文研究了使用生成对抗网络（GANs）在基于物理的益智游戏《愤怒的小鸟》中生成稳定结构的适用性。尽管先前GAN在关卡生成中的应用主要局限于基于图块的表示，但本文探索了其用于创建由多个小方块构成的稳定结构的可行性。这包括将《愤怒的小鸟》关卡描述转换为合适的网格表示并反向转换的详细编码/解码过程，以及利用最先进的GAN架构和训练方法生成全新结构设计。实验结果表明，GAN可成功应用于生成多样化、复杂且稳定的《愤怒的小鸟》结构。