DeepCreativity：利用深度学习技术衡量创造力
DeepCreativity: Measuring Creativity with Deep Learning Techniques
AI总结：
针对机器创造力的自动评估难题，本文基于Margaret Boden的创造力定义，提出无需人工判断、模块化通用的DeepCreativity衡量指标，经19世纪美国诗歌生成案例验证有效。
摘要：
衡量机器创造力是人工智能领域最引人入胜的挑战之一。本文探讨了利用生成学习技术自动评估创造力的可行性。所提出的解决方案无需人工判断，具备模块化特性且适用范围广泛。我们基于Margaret Boden提出的“创造力由价值、新颖性和惊喜感构成”这一定义，引入了一种名为DeepCreativity的新衡量指标。我们以19世纪美国诗歌生成作为案例研究，对该方法（及相关衡量指标）进行了评估，结果表明其具备有效性和表现力。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
