Transforming Exploratory Creativity with DeLeNoX
This work addresses procedural content generation in games, offering a novel approach to exploratory creativity, though it is incremental in combining existing techniques like novelty search and autoencoders.
The authors tackled the problem of autonomous creative artifact generation in constrained spaces by introducing DeLeNoX, a system that alternates between exploration for diversity and transformation using deep learning to evolve its criteria, resulting in the creation of spaceships for 2D arcade games as a demonstration.
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.