Computational creativity is steadily gaining popularity and has become a recognised field of scientific activity. Still, while work in art-performing and artefact-producing computational creativity has greatly advanced, research into the computerisation of other forms of creativity, such as general creative capabilities for computational cognitive agents or automated creative problem-solvers addressing real-world scenarios in a domain-independent way, is lacking far behind and is receiving only limited attention from the community. In fact, over the last years other disciplines have been reporting developments which - when looked at from the perspective of computational creativity - could turn out to be crucial stepping stones for advancing towards closing this gap between "artistic creativity" and "problem-solving creativity" but which went mostly unnoticed by the computational creativity community. In this paper I will have a closer look at the differences between artistic (computational) creativity and problem-solving (computational) creativity, followed by a review of two developments from cognitive modelling and machine learning which serve as cases in point for breakthroughs with potentially high relevance for problem-solving computational creativity research. I will then use these examples to motivate the claim that for both individual researchers as well as the computational creativity community as such the time has come to also focus on problem-solving creativity.
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