  This paper proposes a procedural content generator which evolves Minecraft
buildings according to an open-ended and intrinsic definition of novelty. To
realize this goal we evaluate individuals' novelty in the latent space using a
3D autoencoder, and alternate between phases of exploration and transformation.
During exploration the system evolves multiple populations of CPPNs through
CPPN-NEAT and constrained novelty search in the latent space (defined by the
current autoencoder). We apply a set of repair and constraint functions to
ensure candidates adhere to basic structural rules and constraints during
evolution. During transformation, we reshape the boundaries of the latent space
to identify new interesting areas of the solution space by retraining the
autoencoder with novel content. In this study we evaluate five different
approaches for training the autoencoder during transformation and its impact on
populations' quality and diversity during evolution. Our results show that by
retraining the autoencoder we can achieve better open-ended complexity compared
to a static model, which is further improved when retraining using larger
datasets of individuals with diverse complexities.
