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Generating Mega Man Levels With Generative Adversarial Networks (GANs)

This page presents research in Procedural Content Generation via Machine Learning done by undergraduate student Benjamin Capps as part of Southwestern University's Summer research program SCOPE. The research makes use of the MM-NEAT software package, though neural networks are not evolved. Rather, real-valued latent vectors are evolved and sent as input to pre-trained GANs which output Mega Man level segments. There are two approaches. The OneGAN approach uses a single GAN trained on all Mega Man level segments in a training set derived from the Video Game Level Corpus. The MultiGAN approach instead uses a separate GAN for each type of level segment: horizontal, upward, downward, upper-left corner, upper-right corner, lower-left corner, and lower-right corner. Levels evolved using each approach were compared in a human subject study to see which levels were more fun, challenging, etc. In particular, we were interested in determining which levels looked more as if they were designed by humans. All evolved levels are available to play below by simply clicking a link. The levels can be played after installing Mega Man Maker.

Presentation for Genetic and Evolutionary Computation Conference

Pre-recorded video presentation for the virtual GECCO 2021 conference.

Video Explanation

This is a brief video introduction to the research filmed by Ben.

Presentation at Southwestern University Homecoming

After completing a human subject study, further analyzing the results, and getting our paper accepted to the Genetic and Evolutionary Computation Conference, Ben gave a presentation on the research to an audience at Southwestern University's Homecoming event. Ben also reflects on the SCOPE summer research experience that made this work possible.

OneGAN

The latent vector inputs evolved with this approach were sent to a single GAN trained on all Mega Man level segments from VGLC.

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MultiGAN

The latent vector inputs evolved with this approach were sent to different GANs depending on where the level segment was going to be placed with respect to the previously placed segment. This encourages a more natural, human-like flow between segments.

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Associated Publications


Peer-Reviewed Conference Publications


Extended Abstracts


Technical Reports


Undergraduate Poster Presentations Supervised


Associated Movies and Images


Miscellaneous Content

  • Fall 2020: Procedural Content Generation for Games with Generative Adversarial Networks: Presentation for the Games AI Research Group at Queen Mary University of London (video)
  • Fall 2020: Interactively Evolving Video Game Levels with Generative Adversarial Networks: 403 Lecture for Math and CS Department
  • Fall 2020: Evolving Mega Man Levels with Generative Adversarial Networks: Virtual SCOPE Open House Website
  • Fall 2020: Evolving Lode Runner Levels with Generative Adversarial Networks: Virtual SCOPE Open House Website
  • Summer 2020: Generating Video Game Levels Using AI: SCOPE Research Presentation made by my SCOPE Summer research students to present to other SCOPE students
  • Summer 2019: Playing and Creating Games With Deep Neural Networks: "Mad Science Monday" presentation made by my SCOPE Summer research students to present to other SCOPE students
  • Fall 2018: Evolutionary Computation Applied to Digital Entertainment and the Arts, poster presented at the President's Appreciation Celebration for Southwestern University donors.
  • Fall 2018: Evolving Mario Levels in the Latent Space of a Deep Convolutional Generative Adversarial Network: presented to Southwestern University students as a 107 Lecture.
  • Summer 2018: The machines have taught themselves to make Mario levels, article in Fast Company about my recent GECCO 2018 paper on generating levels for Super Mario Bros.
  • Spring 2018: Bored with your video game? Artificial intelligence could create new levels on the fly, article in Science about my recent GECCO 2018 paper on generating levels for Super Mario Bros.
  • Spring 2018: Doom and Super Mario could be a lot tougher now AI is building levels, article in The Register about my recent GECCO 2018 paper on generating levels for Super Mario Bros.

  • Last Updated: 4/19/2021