It’s been 10 years since the first ever Mario AI Competition, so I return to the world of Super Mario level generation research and catch up one some of the more interesting examples that have arisen in recent years.
You can read the article based on this video on the AI and Games website and on Medium:
This video uses footage of Super Mario Bros. from World of Longplays:
Video footage of 4K Metroid Prime 2 from YouTube channel iamthegr34td3str0y3r:
This video is inspired by the following AI research papers and projects:
Steve Dahlskog and Julian Togelius (2012): Patterns and Procedural Content Generation. Proceedings of the FDG Workshop on Design Patterns in Games (DPG).
Steve Dahlskog and Julian Togelius (2013): Patterns as Objectives for Level Generation. Proceedings of the Workshop on Design Patterns in Games at FDG.
Steve Dahlskog and Julian Togelius (2014): A Multi-level Level Generator. Proceedings of the IEEE Conference on Computational Intelligence and Games (CIG).
Steve Dahlskog, Julian Togelius and Mark J. Nelson (2014): Linear levels through n-grams. Proceedings of Academic MindTrek.
Summerville, A.J., Philip, S., & Mateas, M. (2015). MCMCTS PCG 4 SMB : Monte Carlo Tree Search to Guide Platformer Level Generation.
Summerville, A., & Mateas, M. (2016). Super Mario as a String: Platformer Level Generation Via LSTMs.
Guzdial, M.J., & Riedl, M.O. (2016). Toward Game Level Generation from Gameplay Videos. CoRR, abs/1602.07721.arxiv.org/ftp/arxiv/papers/1602/1602.07721.pdf
Volz, V., Schrum, J., Liu, J., Lucas, S.M., Smith, A.D., & Risi, S. (2018). Evolving mario levels in the latent space of a deep convolutional generative adversarial network. GECCO.
The Mario AI Framework (10th Anniversary Edition)
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#SuperMarioMaker2 #DeepLearning #MachineLearning
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