Toward Game Level Generation from Gameplay Videos
  Algorithms that generate computer game content require game design knowledge.
We present an approach to automatically learn game design knowledge for level
design from gameplay videos. We further demonstrate how the acquired design
knowledge can be used to generate sections of game levels. Our approach
involves parsing video of people playing a game to detect the appearance of
patterns of sprites and utilizing machine learning to build a probabilistic
model of sprite placement. We show how rich game design information can be
automatically parsed from gameplay videos and represented as a set of
generative probabilistic models. We use Super Mario Bros. as a proof of
concept. We evaluate our approach on a measure of playability and stylistic
similarity to the original levels as represented in the gameplay videos.
