DeepCreativity: Measuring Creativity with Deep Learning Techniques

IF 1.9 Q3 COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE
Giorgio Franceschelli, Mirco Musolesi
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引用次数: 4

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.
深度创造力:用深度学习技术衡量创造力
测量机器创造力是人工智能中最令人着迷的挑战之一。本文探讨了使用生成学习技术对创造力进行自动评估的可能性。所提出的解决方案不涉及人的判断,它是模块化的,具有普遍适用性。我们引入了一个新的衡量标准,即深度创造力,基于Margaret Boden对创造力的定义,即由价值、新颖性和惊喜组成。我们通过一个案例研究来评估我们的方法(和相关措施),即19世纪美国诗歌的产生,展示其有效性和表现力。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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来源期刊
Intelligenza Artificiale
Intelligenza Artificiale COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE-
CiteScore
3.50
自引率
6.70%
发文量
13
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