Learning to Blend Computer Game Levels
  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.
