Franceschelli, Giorgio;
Musolesi, Mirco;
(2022)
DeepCreativity: Measuring Creativity with Deep Learning Techniques.
Intelligenza Artificiale
, 16
(2)
pp. 151-163.
10.3233/IA-220136.
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Abstract
Measuring machine creativity is one of the most fascinating challenges in Artificial Intelligence. This paper explores the possibility of using generative learning techniques for automatic assessment of creativity. The proposed solution does not involve human judgement, it is modular and of general applicability. We introduce a new measure, namely DeepCreativity, based on Margaret Boden’s definition of creativity as composed by value, novelty and surprise. We evaluate our methodology (and related measure) considering a case study, i.e., the generation of 19th century American poetry, showing its effectiveness and expressiveness.
| Type: | Article |
|---|---|
| Title: | DeepCreativity: Measuring Creativity with Deep Learning Techniques |
| Open access status: | An open access version is available from UCL Discovery |
| DOI: | 10.3233/IA-220136 |
| Publisher version: | http://dx.doi.org/10.3233/IA-220136 |
| Language: | English |
| Additional information: | This version is the author accepted manuscript. For information on re-use, please refer to the publisher’s terms and conditions. |
| UCL classification: | UCL UCL > Provost and Vice Provost Offices > UCL BEAMS UCL > Provost and Vice Provost Offices > UCL BEAMS > Faculty of Engineering Science UCL > Provost and Vice Provost Offices > UCL BEAMS > Faculty of Engineering Science > Dept of Computer Science |
| URI: | https://discovery.ucl.ac.uk/id/eprint/10164863 |
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