  We introduce DeLeNoX (Deep Learning Novelty Explorer), a system that
autonomously creates artifacts in constrained spaces according to its own
evolving interestingness criterion. DeLeNoX proceeds in alternating phases of
exploration and transformation. In the exploration phases, a version of novelty
search augmented with constraint handling searches for maximally diverse
artifacts using a given distance function. In the transformation phases, a deep
learning autoencoder learns to compress the variation between the found
artifacts into a lower-dimensional space. The newly trained encoder is then
used as the basis for a new distance function, transforming the criteria for
the next exploration phase. In the current paper, we apply DeLeNoX to the
creation of spaceships suitable for use in two-dimensional arcade-style
computer games, a representative problem in procedural content generation in
games. We also situate DeLeNoX in relation to the distinction between
exploratory and transformational creativity, and in relation to Schmidhuber's
theory of creativity through the drive for compression progress.
