系统梳理人机协作的理论与挑战，推动可信赖、可扩展的智能协同
Advancing Human-Machine Teaming: Concepts, Challenges, and Applications
- 构建涵盖决策、信任与自适应的跨领域人机协同分类体系
- 揭示可解释性、角色分配与基准测试等核心难题
- 适合关注智能系统伦理与实际部署的研究者与工程师
人机协同（HMT）正通过集成基于AI的决策、信任校准与自适应团队机制，在国防、医疗与自主系统等领域重塑协作模式。本文提出一个全面的HMT分类框架，分析强化学习、基于实例的学习、互依理论等理论模型，以及跨学科方法。区别于以往综述，本研究深入探讨团队认知、伦理AI、多模态交互与真实场景评估体系。关键挑战包括可解释性、角色分配与可扩展基准测试。文章展望未来研究方向：跨领域适应、可信AI与标准化测试平台。通过融合计算与社会科学，为构建稳健、伦理且可扩展的人机协同系统奠定基础。
原文摘要 · Abstract (English)
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.
Thank you to arXiv for use of its open access interoperability. PaperDance 不是 arXiv 官方产品；中文卡片由大模型生成，请以原文为准。