Zeda Xu, C. Hong, Nicolas F. Soria Zurita, J. Gyory, Gary Stump, H. Nolte, Jonathan Cagan, Christopher McComb
{"title":"通过交流进行适应:评估复杂工程系统设计中的人与人工智能合作关系","authors":"Zeda Xu, C. Hong, Nicolas F. Soria Zurita, J. Gyory, Gary Stump, H. Nolte, Jonathan Cagan, Christopher McComb","doi":"10.1115/1.4064490","DOIUrl":null,"url":null,"abstract":"\n Exploring the opportunities for incorporating Artificial Intelligence (AI) to support team problem solving has been the focus of intensive ongoing research. However, while the incorporation of such AI tools into human team problem solving can improve team performance, it is still unclear what modality of AI integration will lead to a genuine human-AI partnership capable of mimicking the dynamic adaptability of humans. This work unites human designers with AI Partners as fellow team members who can both reactively and proactively collaborate in real-time towards solving a complex and evolving engineering problem. Team performance and problem-solving behaviors are examined using the HyForm collaborative research platform. The problem constraints are unexpectedly changed midway through problem solving to simulate the nature of dynamically evolving engineering problems. This work shows that after the shock is introduced, human-AI hybrid teams perform similarly to human teams, demonstrating the capability of AI Partners to adapt to unexpected events. Nonetheless, hybrid teams do struggle more with coordination and communication after the shock is introduced. Overall, this work demonstrates that these AI design Partners can participate as active partners within human teams during a large, complex task, showing promise for future integration in practice.","PeriodicalId":50137,"journal":{"name":"Journal of Mechanical Design","volume":null,"pages":null},"PeriodicalIF":2.9000,"publicationDate":"2024-01-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Adaptation through Communication: Assessing Human-AI Partnership for the Design of Complex Engineering Systems\",\"authors\":\"Zeda Xu, C. Hong, Nicolas F. Soria Zurita, J. Gyory, Gary Stump, H. Nolte, Jonathan Cagan, Christopher McComb\",\"doi\":\"10.1115/1.4064490\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"\\n Exploring the opportunities for incorporating Artificial Intelligence (AI) to support team problem solving has been the focus of intensive ongoing research. However, while the incorporation of such AI tools into human team problem solving can improve team performance, it is still unclear what modality of AI integration will lead to a genuine human-AI partnership capable of mimicking the dynamic adaptability of humans. This work unites human designers with AI Partners as fellow team members who can both reactively and proactively collaborate in real-time towards solving a complex and evolving engineering problem. Team performance and problem-solving behaviors are examined using the HyForm collaborative research platform. The problem constraints are unexpectedly changed midway through problem solving to simulate the nature of dynamically evolving engineering problems. This work shows that after the shock is introduced, human-AI hybrid teams perform similarly to human teams, demonstrating the capability of AI Partners to adapt to unexpected events. Nonetheless, hybrid teams do struggle more with coordination and communication after the shock is introduced. Overall, this work demonstrates that these AI design Partners can participate as active partners within human teams during a large, complex task, showing promise for future integration in practice.\",\"PeriodicalId\":50137,\"journal\":{\"name\":\"Journal of Mechanical Design\",\"volume\":null,\"pages\":null},\"PeriodicalIF\":2.9000,\"publicationDate\":\"2024-01-12\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Journal of Mechanical Design\",\"FirstCategoryId\":\"5\",\"ListUrlMain\":\"https://doi.org/10.1115/1.4064490\",\"RegionNum\":3,\"RegionCategory\":\"工程技术\",\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q2\",\"JCRName\":\"ENGINEERING, MECHANICAL\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Journal of Mechanical Design","FirstCategoryId":"5","ListUrlMain":"https://doi.org/10.1115/1.4064490","RegionNum":3,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q2","JCRName":"ENGINEERING, MECHANICAL","Score":null,"Total":0}
Adaptation through Communication: Assessing Human-AI Partnership for the Design of Complex Engineering Systems
Exploring the opportunities for incorporating Artificial Intelligence (AI) to support team problem solving has been the focus of intensive ongoing research. However, while the incorporation of such AI tools into human team problem solving can improve team performance, it is still unclear what modality of AI integration will lead to a genuine human-AI partnership capable of mimicking the dynamic adaptability of humans. This work unites human designers with AI Partners as fellow team members who can both reactively and proactively collaborate in real-time towards solving a complex and evolving engineering problem. Team performance and problem-solving behaviors are examined using the HyForm collaborative research platform. The problem constraints are unexpectedly changed midway through problem solving to simulate the nature of dynamically evolving engineering problems. This work shows that after the shock is introduced, human-AI hybrid teams perform similarly to human teams, demonstrating the capability of AI Partners to adapt to unexpected events. Nonetheless, hybrid teams do struggle more with coordination and communication after the shock is introduced. Overall, this work demonstrates that these AI design Partners can participate as active partners within human teams during a large, complex task, showing promise for future integration in practice.
期刊介绍:
The Journal of Mechanical Design (JMD) serves the broad design community as the venue for scholarly, archival research in all aspects of the design activity with emphasis on design synthesis. JMD has traditionally served the ASME Design Engineering Division and its technical committees, but it welcomes contributions from all areas of design with emphasis on synthesis. JMD communicates original contributions, primarily in the form of research articles of considerable depth, but also technical briefs, design innovation papers, book reviews, and editorials.
Scope: The Journal of Mechanical Design (JMD) serves the broad design community as the venue for scholarly, archival research in all aspects of the design activity with emphasis on design synthesis. JMD has traditionally served the ASME Design Engineering Division and its technical committees, but it welcomes contributions from all areas of design with emphasis on synthesis. JMD communicates original contributions, primarily in the form of research articles of considerable depth, but also technical briefs, design innovation papers, book reviews, and editorials.