The procedural content generation algorithms are a well-known concept in the game industry. Because of their time efficiency, there is more effort put into developing new algorithms. The Wave Function Collapse algorithm developed by Max Gumin is populating the pattern from a small sample. The algorithm gained much popularity because of the variety of the outputs generated from only one input. This paper examines whether the wave function collapse can be trained within a use of Deep Convolutional Generative Adversarial Network to get the output starting with the input specified by the user. This would give additional control over the algorithm and allow to specify the spatial distribution of tiles across the solution space.
Keywords: Wave Function Collapse, Procedural City Generation, Machine Learning, Generative Adversarial Network
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