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    Adaptation Through Communication: Assessing Human–Artificial Intelligence Partnership for the Design of Complex Engineering Systems

    Source: Journal of Mechanical Design:;2024:;volume( 146 ):;issue: 008::page 81401-1
    Author:
    Xu, Zeda
    ,
    Hong, Chloe Soohwa
    ,
    Soria Zurita, Nicolás F.
    ,
    Gyory, Joshua T.
    ,
    Stump, Gary
    ,
    Nolte, Hannah
    ,
    Cagan, Jonathan
    ,
    McComb, Christopher
    DOI: 10.1115/1.4064490
    Publisher: The American Society of Mechanical Engineers (ASME)
    Abstract: 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 toward solving a complex and evolving engineering problem. Team performance and problem-solving behaviors are examined using the HyForm collaborative research platform, which uses an online collaborative design environment that simulates a complex interdisciplinary design problem. 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 unexpected design constraints change, or 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.
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      Adaptation Through Communication: Assessing Human–Artificial Intelligence Partnership for the Design of Complex Engineering Systems

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    http://yetl.yabesh.ir/yetl1/handle/yetl/4295704
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    contributor authorXu, Zeda
    contributor authorHong, Chloe Soohwa
    contributor authorSoria Zurita, Nicolás F.
    contributor authorGyory, Joshua T.
    contributor authorStump, Gary
    contributor authorNolte, Hannah
    contributor authorCagan, Jonathan
    contributor authorMcComb, Christopher
    date accessioned2024-04-24T22:41:54Z
    date available2024-04-24T22:41:54Z
    date copyright2/1/2024 12:00:00 AM
    date issued2024
    identifier issn1050-0472
    identifier othermd_146_8_081401.pdf
    identifier urihttp://yetl.yabesh.ir/yetl1/handle/yetl/4295704
    description abstractExploring 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 toward solving a complex and evolving engineering problem. Team performance and problem-solving behaviors are examined using the HyForm collaborative research platform, which uses an online collaborative design environment that simulates a complex interdisciplinary design problem. 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 unexpected design constraints change, or 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.
    publisherThe American Society of Mechanical Engineers (ASME)
    titleAdaptation Through Communication: Assessing Human–Artificial Intelligence Partnership for the Design of Complex Engineering Systems
    typeJournal Paper
    journal volume146
    journal issue8
    journal titleJournal of Mechanical Design
    identifier doi10.1115/1.4064490
    journal fristpage81401-1
    journal lastpage81401-16
    page16
    treeJournal of Mechanical Design:;2024:;volume( 146 ):;issue: 008
    contenttypeFulltext
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    DSpace software copyright © 2002-2015  DuraSpace
    نرم افزار کتابخانه دیجیتال "دی اسپیس" فارسی شده توسط یابش برای کتابخانه های ایرانی | تماس با یابش
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