  We propose a new procedural content generation method which learns iterative
level generators from a dataset of existing levels. The Path of Destruction
method, as we call it, views level generation as repair; levels are created by
iteratively repairing from a random starting level. The first step is to
generate an artificial dataset from the original set of levels by introducing
many different sequences of mutations to existing levels. In the generated
dataset, features are observations of destroyed levels and targets are the
specific actions that repair the mutated tile in the middle of the
observations. Using this dataset, a convolutional network is trained to map
from observations to their respective appropriate repair actions. The trained
network is then used to iteratively produce levels from random starting maps.
We demonstrate this method by applying it to generate unique and playable
tile-based levels for several 2D games (Zelda, Danger Dave, and Sokoban) and
vary key hyperparameters.
