Lode Enhancer: Level Co-creation Through Scaling
This work addresses level design assistance for game designers, but it is incremental as it builds on existing upscaling techniques in a specific domain.
The paper tackles the problem of AI-assisted level design in 2D games by using deep neural networks to upscale low-resolution patches, enabling co-creation across multiple resolutions in a web-based editor. The result includes a qualitative study with 3 designers who enjoyed the tool and provided feedback for improvement.
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.