Utilizing Generative Adversarial Networks for Stable Structure
  Generation in Angry Birds
Frederic Abraham Matthew Stephenson
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.