Machine learning as the enabler of computational creativity

The research article “Towards Machine Learning as an Enabler of Computational Creativity” has recently been accepted for publication in the IEEE Transactions on Artificial Intelligence.

Deborah Mateja and Armin Heinzl have successfully published their first research paper on machine learning based computational creativity in the IEEE Transactions on Artificial Intelligence. The article investigates state-of-the-art creative capabilities and machine learning techniques in the domain of computational creative systems. In their study, the authors consolidate research from the fields of computer science, computational creativity, and information systems that have previously been published separately. Building on a psychological model of human creativity, the paper explains which creative capabilities are already implemented through machine learning techniques in computational creativity systems. In addition, challenges have been identified that provide the potential for improving the inherent creative capabilities of computational creative systems.

The study indicates that computational creative systems based on machine learning are able to extend the previously static and explicit principles of conventional computational creative systems, enabling creative tasks that have previously been the sole domain of human actors. The paper provides a comprehensive overview of the status quo of computational creativity including available innovative applications.