Advancing Human-Machine Teaming: Concepts, Challenges, and Applications

Dian Chen, Han Jun Yoon, Zelin Wan, Nithin Alluru, Sang Won Lee +7 more
3/16/2025
cs.HCcs.AIcs.LG

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Abstract

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.

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Cite this paper

@article{chen2025advancing,
  title  = {Advancing Human-Machine Teaming: Concepts, Challenges, and Applications},
  author = {Dian Chen and Han Jun Yoon and Zelin Wan and Nithin Alluru and Sang Won Lee and Richard He and Terrence J. Moore and Frederica F. Nelson and Sunghyun Yoon and Hyuk Lim and Dan Dongseong Kim and Jin-Hee Cho},
  year   = {2025},
  eprint = {2503.16518},
  archivePrefix = {arXiv},
  url    = {http://arxiv.org/abs/2503.16518v2}
}

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