Advancing Human-Machine Teaming: Concepts, Challenges, and Applications
Fuente:
arXiv
Gespeichert in:
| Hauptverfasser: | , , , , , , , , , , , |
|---|---|
| Format: | Preprint |
| Veröffentlicht: |
2025
|
| Schlagworte: | |
| Online-Zugang: | |
| Tags: |
Tag hinzufügen
Keine Tags, Fügen Sie den ersten Tag hinzu!
|
| _version_ | 1866908351379537920 |
|---|---|
| author | Chen, Dian Yoon, Han Jun Wan, Zelin Alluru, Nithin Lee, Sang Won He, Richard Moore, Terrence J. Nelson, Frederica F. Yoon, Sunghyun Lim, Hyuk Kim, Dan Dongseong Cho, Jin-Hee |
| author_facet | Chen, Dian Yoon, Han Jun Wan, Zelin Alluru, Nithin Lee, Sang Won He, Richard Moore, Terrence J. Nelson, Frederica F. Yoon, Sunghyun Lim, Hyuk Kim, Dan Dongseong Cho, Jin-Hee |
| contents | Human-Machine Teaming (HMT) is revolutionizing collaboration across domains such as defense, healthcare, and autonomous systems by integrating AI-driven decision-making, trust calibration, and adaptive teaming. This survey presents a comprehensive taxonomy of HMT, analyzing theoretical models, including reinforcement learning, instance-based learning, and interdependence theory, alongside interdisciplinary methodologies. Unlike prior reviews, we examine team cognition, ethical AI, multi-modal interactions, and real-world evaluation frameworks. Key challenges include explainability, role allocation, and scalable benchmarking. We propose future research in cross-domain adaptation, trust-aware AI, and standardized testbeds. By bridging computational and social sciences, this work lays a foundation for resilient, ethical, and scalable HMT systems. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2503_16518 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Advancing Human-Machine Teaming: Concepts, Challenges, and Applications Chen, Dian Yoon, Han Jun Wan, Zelin Alluru, Nithin Lee, Sang Won He, Richard Moore, Terrence J. Nelson, Frederica F. Yoon, Sunghyun Lim, Hyuk Kim, Dan Dongseong Cho, Jin-Hee Human-Computer Interaction Artificial Intelligence Machine Learning Human-Machine Teaming (HMT) is revolutionizing collaboration across domains such as defense, healthcare, and autonomous systems by integrating AI-driven decision-making, trust calibration, and adaptive teaming. This survey presents a comprehensive taxonomy of HMT, analyzing theoretical models, including reinforcement learning, instance-based learning, and interdependence theory, alongside interdisciplinary methodologies. Unlike prior reviews, we examine team cognition, ethical AI, multi-modal interactions, and real-world evaluation frameworks. Key challenges include explainability, role allocation, and scalable benchmarking. We propose future research in cross-domain adaptation, trust-aware AI, and standardized testbeds. By bridging computational and social sciences, this work lays a foundation for resilient, ethical, and scalable HMT systems. |
| title | Advancing Human-Machine Teaming: Concepts, Challenges, and Applications |
| topic | Human-Computer Interaction Artificial Intelligence Machine Learning |
| url | https://arxiv.org/abs/2503.16518 |