Transforming Exploratory Creativity with DeLeNoX

Antonios Liapis, Héctor Pérez Martínez, Julian Togelius, Georgios N. Yannakakis

Research output: Conference Article in Proceeding or Book/Report chapterArticle in proceedingsResearchpeer-review


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
Original languageEnglish
Title of host publicationProceedings of the Fourth International Conference on Computational Creativity
Number of pages8
PublisherAAAI Press
Publication date2013
ISBN (Print)978-1-74210-317-4
Publication statusPublished - 2013
EventThe fourth International Conference on Computational Creativity - Sydney, Australia
Duration: 12 Jun 201314 Jun 2013


ConferenceThe fourth International Conference on Computational Creativity
Internet address


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