CHS: Small: Innovation Through Analogical Search

CHS：小型：通过类比搜索进行创新

• 批准号: 1816242
• 负责人: Aniket Kittur
• 金额: $ 50万
• 依托单位: Carnegie-Mellon University
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2018
• 资助国家: 美国
• 起止时间: 2018-08-15 至 2021-07-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1816242&HistoricalAwards=false
• 关键词: CHS; Small; Innovation; Through; Analogical

Comparisons between two things are useful to support creative thinking and reasoning. Many important breakthroughs in science and technology were driven by analogy: observing water led the Greek philosopher Chrysippus to speculate that sound was a wave phenomenon; an analogy to a bicycle allowed the Wright brothers to design a steerable aircraft. Opportunities to find analogical breakthroughs are exploding with the increased availability of online repositories of ideas ranging from scientific papers to patents to the entire web. This project will use computational tools to facilitate searching for analogies drawn from one domain to help with innovative thinking and reasoning in another domain. The goal is to dramatically accelerate innovation and discovery across domains as diverse as science, humanities, mathematics, law, and design. The planned research bridges the gap between the power of large-scale text mining approaches, which excel at detecting surface similarity, and the depth of human cognition, which is currently unsurpassed at detecting deep analogical similarity. Investigators will explore how representations with weaker structures are robust to issues of language complexity, hierarchies of purposes, and levels of abstraction that are present in real-world documents like research papers and R&D documents. Using these representations, investigators will build computational tools enabling users to connect problems in one field with solutions from another field based on deep structure. Results and algorithms could spur the development of new types of machine-learning techniques that focus on deep structure, and may contribute back to theory in the fields of creativity, problem solving, and innovation.This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

两件事之间的比较对于支持创造性思维和推理很有用。科学技术领域的许多重要突破都是由类比推动的：对水的观察使希腊哲学家克里西普斯推测声音是一种波动现象；与自行车的类比使得莱特兄弟能够设计出可操纵的飞机。随着从科学论文到专利到整个网络的在线思想存储库的可用性不断增加，寻找类比突破的机会正在爆炸式增长。该项目将使用计算工具来促进从一个领域中搜索类比，以帮助在另一个领域中进行创新思维和推理。目标是大幅加速科学、人文、数学、法律和设计等不同领域的创新和发现。计划中的研究弥合了大规模文本挖掘方法与人类认知深度之间的差距，大规模文本挖掘方法擅长检测表面相似性，而人类认知深度目前在检测深层类比相似性方面无与伦比。研究人员将探索结构较弱的表示如何对现实世界文档（如研究论文和研发文档）中存在的语言复杂性、目的层次结构和抽象级别问题具有鲁棒性。使用这些表示，研究人员将构建计算工具，使用户能够将一个领域的问题与基于深层结构的另一领域的解决方案联系起来。结果和算法可以刺激专注于深层结构的新型机器学习技术的发展，并可能对创造力、问题解决和创新领域的理论做出贡献。该奖项反映了 NSF 的法定使命，并通过使用基金会的智力价值和更广泛的影响审查标准进行评估，被认为值得支持。

项目成果

Scaling up analogical innovation with crowds and AI

• DOI: 10.1073/pnas.1807185116
• 发表时间: 2019-02-05
• 期刊: PROCEEDINGS OF THE NATIONAL ACADEMY OF SCIENCES OF THE UNITED STATES OF AMERICA
• 影响因子: 11.1
• 作者: Kittur, Aniket;Yu, Lixiu;Shahaf, Dafna
• 通讯作者: Shahaf, Dafna