DeepCreativity: measuring creativity with deep learning techniques

被引：2

作者：

Franceschelli, Giorgio ^{[1
]}

Musolesi, Mirco ^{[1
,2
,3
]}

机构：

[1] Alma Mater Studiorum Univ Bologna, Bologna, Italy

[2] UCL, London, England

[3] Alan Turing Inst, London, England

来源：

INTELLIGENZA ARTIFICIALE | 2022年 / 16卷 / 02期

关键词：

Computational creativity; deep learning; creativity measure; American poetry;

D O I：

10.3233/IA-220136

中图分类号：

TP18 [人工智能理论];

学科分类号：

081104 ; 0812 ; 0835 ; 1405 ;

摘要：

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.

引用

页码：151 / 163

页数：13

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