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Utilizing Generative Adversarial Networks for Stable Structure Generation in Angry Birds

arXiv 2309.02614 ↗First posted 5 Sep 2023cs.LG cs.AI cs.NEcited by 0

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Abstract (arXiv)

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.

Key facts. No citing papers in the map yet (data through 7 Oct 2026). Builds on 23 of its 48 references that are in the map. First posted on arXiv 5 Sep 2023.

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References · 23

  1. 2016Layer Normalizationcited by 5,579
  2. 2017Progressive Growing of GANs for Improved Quality, Stability, and Variationcited by 2,967
  3. 2016Improved Techniques for Training GANscited by 2,938
  4. 2017Improved Training of Wasserstein GANscited by 2,223
  5. 2014Conditional Generative Adversarial Netscited by 2,152
  6. 2015Unsupervised Representation Learning with Deep Convolutional Generative Adversarial Networkscited by 2,048
  7. 2017Unsupervised Anomaly Detection with Generative Adversarial Networks to Guide Marker Discoverycited by 566
  8. 2015Empirical Evaluation of Rectified Activations in Convolutional Networkcited by 515
  9. 2016Coupled Generative Adversarial Networkscited by 216
  10. 2020A Review on Generative Adversarial Networks: Algorithms, Theory, and Applicationscited by 209
  11. 2017Procedural Content Generation via Machine Learning (PCGML)cited by 136
  12. 2020A Survey on Generative Adversarial Networks: Variants, Applications, and Trainingcited by 97
11 more
  1. 2018Evolving Mario Levels in the Latent Space of a Deep Convolutional Generative Adversarial Networkcited by 76
  2. 2020Generative Adversarial Networks for Image and Video Synthesis: Algorithms and Applicationscited by 48
  3. 2017Adversarial Generation of Natural Languagecited by 33
  4. 2018An Introduction to Image Synthesis with Generative Adversarial Netscited by 32
  5. 2018DOOM Level Generation using Generative Adversarial Networkscited by 15
  6. 2019It GAN DO Better: GAN-based Detection of Objects on Images with Varying Qualitycited by 12
  7. 2020Audio Codec Enhancement with Generative Adversarial Networkscited by 11
  8. 2017DeepMasterPrints: Generating MasterPrints for Dictionary Attacks via Latent Variable Evolutioncited by 7
  9. 2021Level Generation for Angry Birds with Sequential VAE and Latent Variable Evolutioncited by 6
  10. 2021Deceptive Level Generation for Angry Birdscited by 5
  11. 2021Generative adversarial network with object detector discriminator for enhanced defect detection on ultrasonic B-scanscited by 2

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