论文标题
深刻性：通过深度学习技术来衡量创造力
DeepCreativity: Measuring Creativity with Deep Learning Techniques
论文作者
论文摘要
测量机器的创造力是人工智能中最迷人的挑战之一。本文探讨了使用生成学习技术自动评估创造力的可能性。提出的解决方案不涉及人类判断，它是模块化的，并且具有一般适用性。我们基于玛格丽特·博登（Margaret Boden）对创造力的定义，即价值，新颖性和惊喜构成的创造力的定义，提出了一种新的措施，即深刻的措施。我们考虑了一个案例研究，即19世纪美国诗歌的产生，评估了我们的方法论（及相关措施），显示出其有效性和表现力。
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.
