Human Versus Artificial Intelligence: A Data-Driven Approach to Real-Time Process Management During Complex Engineering Design
| Autor: | Nicolás F. Soria Zurita, Corey Balon, Kenneth Kotovsky, Jonathan Cagan, Jay D. Martin, Christopher McComb, Joshua T. Gyory |
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| Rok vydání: | 2021 |
| Předmět: | |
| Zdroj: | Journal of Mechanical Design. 144 |
| ISSN: | 1528-9001 1050-0472 |
| DOI: | 10.1115/1.4052488 |
| Popis: | Managing the design process of teams has been shown to considerably improve problem-solving behaviors and resulting final outcomes. Automating this activity presents significant opportunities in delivering interventions that dynamically adapt to the state of a team in order to reap the most impact. In this work, an artificial intelligence (AI) agent is created to manage the design process of engineering teams in real time, tracking features of teams’ actions and communications during a complex design and path-planning task in multidisciplinary teams. Teams are also placed under the guidance of human process managers for comparison. Regarding outcomes, teams perform equally as well under both types of management, with trends toward even superior performance from the AI-managed teams. The managers’ intervention strategies and team perceptions of those strategies are also explored, illuminating some intriguing similarities. Both the AI and human process managers focus largely on communication-based interventions, though differences start to emerge in the distribution of interventions across team roles. Furthermore, team members perceive the interventions from both the AI and human manager as equally relevant and helpful, and believe the AI agent to be just as sensitive to the needs of the team. Thus, the overall results show that the AI manager agent introduced in this work is able to match the capabilities of humans, showing potential in automating the management of a complex design process. |
| Databáze: | OpenAIRE |
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