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Category Archives: Procedural Content Generation

CPPN2GAN: Combining compositional pattern-producing networks and GANs for large-scale pattern generation

Interactive evolution and exploration within latent level-design space of generative adversarial networks

Increasing generality in machine learning through procedural content generation

Bootstrapping conditional GANs for video game level generation

Capturing local and global patterns in procedural content generation via machine learning

Like an artificial Intelligence in a candy store

We auto-generated hundreds of match-3 levels for Candy Crush Saga using different methods to see if we could pass them off as human-generated levels. Capturing patterns and creating symmetry were the main ingredients of the succeeding algorithm.  If you’ve played a lot of Candy Crush Saga, you might be able to answer this: Which of …

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How to replace hours of development work using ML bots

Goodgame Studios: A modl.ai case study Big Farm: Home and Garden is a free to play home design themed, match-3 puzzle game developed by Hamburg-based Goodgame Studios. They approached the team at modl.ai to see if there was a better solution using data-driven artificial intelligence (AI) to replace hours of development work. Platforms Android iOS …

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