An Empirical Exploration of a Definition of Creative Novelty for Generative Art

  • Taras Kowaliw
  • Alan Dorin
  • Jon McCormack
Part of the Lecture Notes in Computer Science book series (LNCS, volume 5865)

Abstract

We explore a new definition of creativity — one which emphasizes the statistical capacity of a system to generate previously unseen patterns — and discuss motivations for this perspective in the context of machine learning. We show the definition to be computationally tractable, and apply it to the domain of generative art, utilizing a collection of features drawn from image processing. We next utilize our model of creativity in an interactive evolutionary art task, that of generating biomorphs. An individual biomorph is considered a potentially creative system by considering its capacity to generate novel children. We consider the creativity of biomorphs discovered via interactive evolution, via our creativity measure, and as a control, via totally random generation. It is shown that both the former methods find individuals deemed creative by our measure; Further, we argue that several of the discovered “creative” individuals are novel in a human-understandable way. We conclude that our creativity measure has the capacity to aid in user-guided evolutionary tasks.

Keywords

Feature Space Empirical Exploration Phenotypic Space Creativity Lite Creative System 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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Copyright information

© Springer-Verlag Berlin Heidelberg 2009

Authors and Affiliations

  • Taras Kowaliw
    • 1
  • Alan Dorin
    • 1
  • Jon McCormack
    • 1
  1. 1.Centre for Electronic Media Art, Faculty of Information TechnologyMonash UniversityClaytonAustralia

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