Creativity through Collaborative Human-Machine Interactions: A Formal Approach to Design Crowd Sourcing

通过人机协作交互发挥创造力：众包设计的正式方法

• 批准号: 1266184
• 负责人: Panos Papalambros
• 金额: $ 64.26万
• 依托单位: Regents of the University of Michigan - Ann Arbor
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2013
• 资助国家: 美国
• 起止时间: 2013-08-01 至 2017-07-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1266184&HistoricalAwards=false
• 关键词: Creativity; through; Collaborative; Human; Machine

This research project will create a theoretical and computational infrastructure to design new technology using creativity from crowds and individuals in combination with machine learning. A crowd could be a collection of experts within an organization, a classroom of students, or a large number of people online. Earlier research used machine learning working with individual engineers to help with simple design problems. This research will extend the earlier work to more complex configuration design problems, and will add crowd sourcing. Design representations will be graphs instead of vectors, and the design space will not be defined ahead of time. Machine learning may prove to be an important improvement over design evolution methods, and will provide insight into which design features are important. Machine learning also generates a model of subjective human judgment and preference, leading to more efficient and perhaps more innovative design synthesis. If successful, this research will provide a platform for new models of innovation with input from multiple stakeholders: A machine supported by crowd-sourced knowledge and real-time interaction with humans will be able to produce unique and creative structures that were beyond the imagination of the humans involved. This research will also result in algorithmic advances in inference, learning, and related optimization techniques in representation learning for structured data and interactive human-machine collaboration. It will also provide a mathematical framework for innovation in massive system design problems involving thousands of designers such as the design of a jet fighter. The technology developed will be possible to implement in a variety of environments from complex engineering system design decisions to ideas for new products on commercial sites. In an educational context, this research, if successful, will allow students and teachers to experiment with design tools, individually, as part of a classroom experience, or as a national endeavor to both learn and possibly to generate new design ideas collectively.

该研究项目将创建一个理论和计算基础设施，利用群体和个人的创造力结合机器学习来设计新技术。 人群可以是一个组织内的专家集合，一个学生教室，或在线的大量人员。早期的研究使用机器学习与个别工程师合作来帮助解决简单的设计问题。 这项研究将把早期的工作扩展到更复杂的配置设计问题，并将增加众包。 设计表示将是图形而不是矢量，设计空间将不会提前定义。机器学习可能被证明是对设计进化方法的重要改进，并将提供对哪些设计特征重要的洞察。 机器学习还可以生成主观人类判断和偏好的模型，从而实现更高效、更创新的设计合成。如果成功，这项研究将为多个利益相关者的新创新模式提供一个平台：由众包知识和与人类实时互动支持的机器将能够产生超出人类想象的独特和创造性的结构。这项研究还将导致推理，学习和相关优化技术的算法进步，用于结构化数据和交互式人机协作的表示学习。它还将为涉及数千名设计师的大规模系统设计问题（如喷气式战斗机的设计）的创新提供数学框架。开发的技术将有可能在各种环境中实施，从复杂的工程系统设计决策到商业场所的新产品创意。在教育背景下，这项研究，如果成功的话，将允许学生和教师单独实验设计工具，作为课堂体验的一部分，或作为一个国家的奋进，既学习，并可能产生新的设计理念集体。