Lode Enhancer: Level Co-creation Through Scaling
Debosmita Bhaumik Julian Togelius Georgios N. Yannakakis Ahmed Khalifa
We explore AI-powered upscaling as a design assistance tool in the context of
creating 2D game levels. Deep neural networks are used to upscale artificially
downscaled patches of levels from the puzzle platformer game Lode Runner. The
trained networks are incorporated into a web-based editor, where the user can
create and edit levels at three different levels of resolution: 4x4, 8x8, and
16x16. An edit at any resolution instantly transfers to the other resolutions.
As upscaling requires inventing features that might not be present at lower
resolutions, we train neural networks to reproduce these features. We introduce
a neural network architecture that is capable of not only learning upscaling
but also giving higher priority to less frequent tiles. To investigate the
potential of this tool and guide further development, we conduct a qualitative
study with 3 designers to understand how they use it. Designers enjoyed
co-designing with the tool, liked its underlying concept, and provided feedback
for further improvement.