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Awesome PCGML

Mario



๐Ÿ“š Books and Surveys

  • A. Summerville et al., "Procedural Content Generation via Machine Learning (PCGML)
  • Guzdial, M., Snodgrass, S., & Summerville, A. J. (2022). Procedural Content Generation Via Machine Learning: An Overview
  • Togelius, J., Shaker, N., & Nelson, M. J. (2014). Procedural content generation in games: A textbook and an overview of current research

๐ŸŽฎ Level Generation

  • Jadhav, M., & Guzdial, M. (2021, October). Tile embedding: a general representation for level generation
  • Sarkar, A., Yang, Z., & Cooper, S. (2020). Controllable level blending between games using variational autoencoders
  • Chen, E., Sydora, C., Burega, B., Mahajan, A., Abdullah, A., Gallivan, M., & Guzdial, M. (2020). Image-to-Level: Generation and Repair
  • Awiszus, M., Schubert, F., & Rosenhahn, B. (2020, October). TOAD-GAN: Coherent style level generation from a single example
  • Summerville, A., & Mateas, M. (2016). Super Mario as a string: Platformer level generation via lstms
  • Mirgati, N., & Guzdial, M. (2023, August). Joint Level Generation and Translation Using Gameplay Videos. In 2023 IEEE Conference on Games (CoG) (pp. 1-10). IEEE.
  • Halina, E., & Guzdial, M. (2023). Tree-Based Reconstructive Partitioning: A Novel Low-Data Level Generation Approach. Proceedings of the AAAI Conference on Artificial Intelligence and Interactive Digital Entertainment, 19(1), 244-254. https://doi.org/10.1609/aiide.v19i1.27520

๐ŸŽจ Asset Generation

  • Saravanan, A., & Guzdial, M. (2022). Pixel vq-vaes for improved pixel art representation
  • Gonzalez, A., Guzdial, M., & Ramos, F. (2020). Generating gameplay-relevant art assets with transfer learning
  • Loftsdottir, Dagmar, and Matthew Guzdial. "SketchBetween: Video-to-Video Synthesis for Sprite Animation via Sketches

๐Ÿง  Reinforcement Learning

  • Khalifa, Ahmed, et al. "Pcgrl: Procedural content generation via reinforcement learning."
  • Mahmoudi-Nejad, A., Guzdial, M., & Boulanger, P. (2021, October). Arachnophobia exposure therapy using experience-driven procedural content generation via reinforcement learning (EDPCGRL)

๐Ÿ‘ฉโ€๐Ÿ’ป Mixed-Initiative (Co-creative)

  • Guzdial, M., Liao, N., & Riedl, M. (2018). Co-creative level design via machine learning
  • Zhou, Z., & Guzdial, M. (2021, August). Toward co-creative dungeon generation via transfer learning

๐Ÿ”Ž Explainability

  • Guzdial, M., Reno, J., Chen, J., Smith, G., & Riedl, M. (2018). Explainable PCGML via game design patterns

๐Ÿ“œ Quests

  • Kristen, K. Y., Sturtevant, N. R., & Guzdial, M. (2020). What is a Quest?. In AIIDE Workshops.

๐Ÿ“Š Datasets

  • Summerville, A. J., Snodgrass, S., Mateas, M., & Ontanรณn, S. (2016). The vglc: The video game level corpus
  • Snodgrass, S., Summerville, A., & Ontanon, S. (2021). Studying the Effects of Training Data on Machine Learning-Based Procedural Content Generation

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