Video Game Level Repair via Mixed Integer Linear Programming
  Recent advancements in procedural content generation via machine learning
enable the generation of video-game levels that are aesthetically similar to
human-authored examples. However, the generated levels are often unplayable
without additional editing. We propose a generate-then-repair framework for
automatic generation of playable levels adhering to specific styles. The
framework constructs levels using a generative adversarial network (GAN)
trained with human-authored examples and repairs them using a mixed-integer
linear program (MIP) with playability constraints. A key component of the
framework is computing minimum cost edits between the GAN generated level and
the solution of the MIP solver, which we cast as a minimum cost network flow
problem. Results show that the proposed framework generates a diverse range of
playable levels, that capture the spatial relationships between objects
exhibited in the human-authored levels.
