  This paper introduces a surrogate model of gameplay that learns the mapping
between different game facets, and applies it to a generative system which
designs new content in one of these facets. Focusing on the shooter game genre,
the paper explores how deep learning can help build a model which combines the
game level structure and the game's character class parameters as input and the
gameplay outcomes as output. The model is trained on a large corpus of game
data from simulations with artificial agents in random sets of levels and class
parameters. The model is then used to generate classes for specific levels and
for a desired game outcome, such as balanced matches of short duration.
Findings in this paper show that the system can be expressive and can generate
classes for both computer generated and human authored levels.
