Transforming Exploratory Creativity with DeLeNoX

TL;DR

DeLeNoX is an autonomous system that explores and transforms artifact spaces using novelty search and deep learning autoencoders, enabling procedural content generation for game spaceships and advancing understanding of creative processes.

Contribution

The paper introduces DeLeNoX, a novel system combining exploration and transformation phases with autoencoders to enhance creative artifact generation.

Findings

Successfully generated diverse spaceship designs for games

Demonstrated the system's ability to evolve interestingness criteria

Connected the approach to theories of exploratory and transformational creativity

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.