Learning to Blend Computer Game Levels
This work addresses level design automation for game developers, but it is incremental as it builds on existing probabilistic modeling and analogical reasoning techniques.
The paper tackles the problem of generating novel computer game levels by blending different game concepts unsupervised, using analogical reasoning on probabilistic models learned from gameplay videos, and demonstrates its ability to explain human-blended levels in Super Mario Bros.
We present an approach to generate novel computer game levels that blend different game concepts in an unsupervised fashion. Our primary contribution is an analogical reasoning process to construct blends between level design models learned from gameplay videos. The models represent probabilistic relationships between elements in the game. An analogical reasoning process maps features between two models to produce blended models that can then generate new level chunks. As a proof-of-concept we train our system on the classic platformer game Super Mario Bros. due to its highly-regarded and well understood level design. We evaluate the extent to which the models represent stylistic level design knowledge and demonstrate the ability of our system to explain levels that were blended by human expert designers.