Transforming exploratory creativity with DeLeNoX

Abstract

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.