Utilizing Generative Adversarial Networks for Stable Structure Generation in Angry Birds
This addresses level generation for puzzle games like Angry Birds, but it is incremental as it adapts existing GAN methods to a new representation.
The paper tackled the problem of generating stable structures for the physics-based game Angry Birds using Generative Adversarial Networks (GANs), and the result showed that GANs can successfully produce a varied range of complex and stable structures.
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.