DeepCreativity: Measuring Creativity with Deep Learning Techniques
This addresses the problem of automated creativity assessment for AI researchers, but it is incremental as it applies existing generative learning techniques to a specific domain.
The paper tackled the challenge of measuring machine creativity by proposing DeepCreativity, a measure based on value, novelty, and surprise, and evaluated it on 19th century American poetry generation, showing effectiveness and expressiveness.
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.