Game Level Blending using a Learned Level Representation
Venkata Sai Revanth Atmakuri Seth Cooper Matthew Guzdial
Game level blending via machine learning, the process of combining features
of game levels to create unique and novel game levels using Procedural Content
Generation via Machine Learning (PCGML) techniques, has gained increasing
popularity in recent years. However, many existing techniques rely on
human-annotated level representations, which limits game level blending to a
limited number of annotated games. Even with annotated games, researchers often
need to author an additional shared representation to make blending possible.
In this paper, we present a novel approach to game level blending that employs
Clustering-based Tile Embeddings (CTE), a learned level representation
technique that can serve as a level representation for unannotated games and a
unified level representation across games without the need for human
annotation. CTE represents game level tiles as a continuous vector
representation, unifying their visual, contextual, and behavioral information.
We apply this approach to two classic Nintendo games, Lode Runner and The
Legend of Zelda. We run an evaluation comparing the CTE representation to a
common, human-annotated representation in the blending task and find that CTE
has comparable or better performance without the need for human annotation.