CAREER: Problem Partitioning and Division of Labor for Human-Computer Collaboration in Engineering Design

职业：工程设计中人机协作的问题划分与分工

• 批准号: 2339546
• 负责人: Alparslan Bayrak
• 金额: $ 55.78万
• 依托单位: Lehigh University
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2024
• 资助国家: 美国
• 起止时间: 2024-09-01 至 2029-08-31
• 项目状态: 未结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=2339546&HistoricalAwards=false
• 关键词: CAREER; Problem; Partitioning; Division; Labor

The objective of this Faculty Early Career Development (CAREER) research project is to understand how the allocation of collective design tasks into teams of humans and Artificial Intelligence (AI)-enabled computer agents with diverse decision-making characteristics will affect design outcomes. The overarching premise of this research project is that the current organization of design teams around analysis disciplines (e.g., thermal analysis, structural analysis) or around physical components (e.g., engine, battery, exterior body) may not be the ideal way to architect human-AI teams. Considering the differences between humans and AI systems in terms of their capabilities, there may be alternative ways to divide tasks and responsibilities in system-level design problems among humans and artificial members within a team. This research will contribute to the field of design science through a systematic and comprehensive analysis of team architectures in hybrid human-AI teams and the impact of those different architectures on design outcomes. Rather than assuming a pre-defined role for AI, this project will follow a top-down Systems Engineering approach to identify best practices for defining roles for AI in a design team using computational models of human and AI decision-making processes. An experimental study using a video game platform will engage human users to work alongside AI teammates in solving a design problem to assess the impact of human factors in this context as well. The findings will inform how AI technology should be integrated into the engineering design workforce with proper task allocation in order to reduce system development time and costs for future enterprises in multiple industries, spanning from smart healthcare to defense. The project will generate broader impacts by engaging a diverse group of undergraduate students into research using the STEM-SI program at Lehigh University. Integrated with the research program, the education plan will improve pedagogical practices in statistics and machine learning for engineering systems applications using e-training games. Outreach workshops within local communities will attract broader interest in data science by engaging middle school girls in data-related challenges in engineering using the CHOICES program at Lehigh University. This research addresses the lack of fundamental principles to guide task partitioning and division of labor for hybrid human-AI teams, accounting not only for heterogeneity among decision-makers (represented by a select set of characteristics), but also for important human factors. The project will use multi-agent simulations to model generalized agents that solve context-free design problems following Bayesian decision-making processes. These agents will be characterized in terms of task performance, self-confidence, and confidence in other team members. A computational analysis will use various problem partitioning and task assignment strategies from decomposition-based design and machine learning to quantify their impact on team collaboration considering diverse agent characteristics. Mirroring this simulation scenario, behavioral experiments on an electric vehicle design and control game will present a collaborative design decision-making problem for hybrid human-AI teams with alternative task allocation scenarios in a controlled setting. These experiments will collect behavioral data that capture the effects of human factors, including bias, workload, and job satisfaction, and that will be used to validate or refine the computational findings. As a by-product, this research will develop an open infrastructure to study human-AI collaboration in design teams by sharing the experimental platform with the broader scientific community. This project will also integrate video game platforms developed for the behavioral study into existing mechanical engineering courses and data bootcamps to teach undergraduate and graduate students data analytics. Outreach workshops will use the same game platforms to increase data literacy and promote STEM careers among middle school girls.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.

这个教师早期职业发展（职业）研究项目的目的是了解集体设计任务如何分配到人类和人工智能团队（AI）支持具有不同决策特征的计算机代理将影响设计成果。该研究项目的总体前提是，目前围绕分析学科的设计团队（例如，热分析，结构分析）或周围的物理组件（例如，发动机，电池，外部机构）可能不是建造人类AI团队的理想方式。考虑到人类和人工智能系统在能力方面的差异，可能还有其他方法可以将团队中人类和人造成员之间的系统级设计问题分配任务和责任。这项研究将通过对混合人类团队中的团队体系结构进行系统和全面的分析以及这些不同体系结构对设计成果的影响，从而为设计科学的领域做出贡献。该项目不是假定AI的预定角色，而是遵循自上而下的系统工程方法，以使用人和AI决策过程的计算模型来确定在设计团队中定义AI角色的最佳实践。使用视频游戏平台的实验研究将吸引人类用户与AI队友一起解决设计问题，以评估在这种情况下人为因素的影响。这些发现将告知应如何通过适当的任务分配将AI技术集成到工程设计员工中，以减少从智能医疗保健到国防的多个行业中未来企业的系统开发时间和成本。该项目将使用Lehigh University的STEM-SI计划参与研究，从而产生更广泛的影响。与研究计划集成在一起，教育计划将改善使用电子培训游戏的工程系统应用程序的统计和机器学习中的教学实践。当地社区内的推广讲习班将通过利哈伊大学的选择计划使中学女孩参与与数据相关的工程挑战，从而吸引对数据科学的广泛兴趣。这项研究解决了缺乏基本原则来指导混合人类AI团队的任务分配和劳动分工，这不仅是针对决策者之间的异质性（由一组特征代表的），还为重要的人为因素而进行。该项目将使用多代理模拟来模拟贝叶斯决策过程后解决无上下文设计问题的通用代理。这些代理人将以任务绩效，自信和对其他团队成员的信心来表征。计算分析将使用基于分解的设计和机器学习的各种问题分配和任务分配策略，以量化其对团队协作的影响，以考虑多样化的代理特征。在反映这种仿真方案的情况下，电动汽车设计和控制游戏上的行为实验将为混合人类AI团队提供协作设计决策问题，并在受控的环境中具有替代性的任务分配方案。这些实验将收集捕获人为因素（包括偏见，工作量和工作满意度）的影响的行为数据，并将用于验证或完善计算结果。作为副产品，这项研究将通过与更广泛的科学界共享实验平台来开发开放的基础设施来研究设计团队中的人类合作。该项目还将集成为行为研究开发的视频游戏平台，以纳入现有的机械工程课程和数据引导训练营，以教授本科生和研究生数据分析。外展研讨会将使用相同的游戏平台来提高数据素养并促进中学女生的STEM职业。该奖项反映了NSF的法定任务，并被认为是值得通过基金会的知识分子优点和更广泛的影响评估标准通过评估来获得支持的。