Procedural Content Generation of Puzzle Games using Conditional Generative Adversarial Networks
Andreas Hald, Jens Struckmann Hansen, Jeppe Theiss Kristensen, Paolo Burelli · 2020
In this article, we present an experimental approach to using parameterized Generative Adversarial Networks (GANs) to produce levels for the puzzle game Lily’s Garden1. We extract two condition-vectors from the real levels in an effort to control the details of the GAN’s outputs. While the GANs performs well in approximating the first condition (map-shape), they struggle to approximate the second condition (piece distribution). We hypothesize that this might be improved by trying out alternative architectures for both the Generator and Discriminator of the GANs.