Lode Enhancer: Level Co-creation Through Scaling

被引:1
|
作者
Bhaumik, Debosmita [1 ]
Togelius, Julian [2 ,3 ]
Yannakakis, Georgios N. [1 ,3 ]
Khalifa, Ahmed [1 ]
机构
[1] Inst Digital Games, Msida, Malta
[2] Game Innovat Lab, Brooklyn, NY USA
[3] Modl Ai, Copenhagen, Denmark
来源
PROCEEDINGS OF THE 18TH INTERNATIONAL CONFERENCE ON THE FOUNDATIONS OF DIGITAL GAMES, FDG 2023 | 2023年
关键词
neural networks; mixed-initiative; supervised learning; procedural content generation; upscaling;
D O I
10.1145/3582437.3587206
中图分类号
J [艺术];
学科分类号
13 ; 1301 ;
摘要
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.
引用
收藏
页数:8
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