  In this article, we present an experimental approach to using parameterized
Generative Adversarial Networks (GANs) to produce levels for the puzzle game
Lily's Garden. We extract two condition vectors from the real levels in an
effort to control the details of the GAN's outputs. While the GANs perform 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.
