Terrain-adaptive PCGML in Minecraft

IEEE Conference on Games (CoG), pp. 1–8

Abstract

We make a first step towards terrain-adaptive PCGML in Minecraft by introducing an automated system to create “volume-to-volume” datasets suitable for machine learning by leveraging handwritten black-box Minecraft settlement generation algorithms. Using this system, we create ten terrain-adaptive Minecraft ML datasets - including ones based on the currently best-performing algorithm submitted to the Generative Design in Minecraft (GDMC) competition. Finally, we train and qualitatively evaluate various GAN-based volume-to-volume models on all ten of our datasets. Although we do not obtain good results in all cases, we demonstrate that terrain-adaptive PCGML in Minecraft is indeed feasible.

Authors 3

1. Arthur van der Staaij Aachen

   Leiden University

   Affiliation as printed

   LIACS, Universiteit Leiden,Leiden,The Netherlands

2. Mike Preuß Aachen

   Leiden University

   Affiliation as printed

   LIACS, Universiteit Leiden,Leiden,The Netherlands

3. Christoph Salge

   University of Hertfordshire

   Affiliation as printed

   SPECS, University of Hertfordshire,Hatfield,UK