人机融合智能决策：概念、框架与应用

李哲; 王可; 王彪; 赵梓棋; 李亚飞; 郭毅博; 胡亚洲; 王华; 吕培; 徐明亮

doi:10.11999/JEIT250260

人机融合智能决策：概念、框架与应用

doi: 10.11999/JEIT250260 cstr: 32379.14.JEIT250260

李哲¹,
王可^{1, 2, 3, ,},
王彪¹,
赵梓棋¹,
李亚飞^{1, 2, 3},
郭毅博^{1, 2, 3},
胡亚洲^{1, 2, 3},
王华^{1, 2, 3},
吕培^{1, 2, 3},
徐明亮^{1, 2, 3}

1.
郑州大学计算机与人工智能学院郑州 450001
2.
国家超级计算郑州中心郑州 450001
3.
智能集群系统教育部工程研究中心郑州 450001

基金项目: 国家自然科学基金(62325602, 62036010, 62372416)，中国博士后科学基金(2020M682348)，国防科技工业海洋防务技术创新中心创新基金(JJ-2022-709-01)，河南省自然科学基金项目(242300421215)

详细信息

作者简介:
李哲：女，博士生，研究方向为智能感知与学习等

王可：男，博士，副教授，研究方向为人工智能、机器学习与优化算法、人机融合智能理论和应用等

王彪：男，硕士生，研究方向为生成式人工智能

赵梓棋：男，硕士生，研究方向为内容生成

李亚飞：男，博士，教授，研究方向为群体智能、机器学习等

郭毅博：男，博士，副教授，研究方向为人工智能、调度优化等

胡亚洲：男，博士，副研究员，研究方向为群体智能、机器人自主学习等

王华：女，博士，教授，研究方向为集群行为计算与仿真、虚拟现实等

吕培：男，博士，教授，研究方向为人工智能、虚拟现实、人机融合智能系统等

徐明亮：男，博士，教授，研究方向为人工智能、智能图形学等

通讯作者:
王可　iekwang@zzu.edu.cn

2¹⁾ https://www.toyota.com.cn/mediacenter/show.php?newsid=5043
1²⁾https://www.tableau.com³⁾https://www.tellius.com⁴⁾https://www.dataiku.com/⁵⁾https://www.palantir.com/platforms/aip/
3⁶⁾https://www.baesystems.com/en-us/product/insight-multi-intelligence-sensor-fusion⁷⁾https://www.palantir.com/platforms/gotham/⁸⁾https://www.army.mil/article/274043/army_enters_into_development_phase_of_the_joint_targeting_fires_product⁹⁾https://www.leidos.com/sites/leidos/files/2021-10/AFATDS-Fact-Sheet-Digital-2021.pdf¹⁰⁾https://www.lockheedmartin.com/en-us/products/aegis-combat-system.html¹¹⁾https://www.lockheedmartin.com/en-us/products/command-control-battle-management-communications-c2bmc.html¹²⁾https://www.intelink.gov/wiki/Net-Centric_Enterprise_Services¹³⁾https://gdmissionsystems.com/command-and-control/command-post-of-the-future
4¹⁴⁾https://www.palantir.com/platforms/gotham/¹⁵⁾https://www.aitheon.com/
中图分类号: TN911.7; TP18
计量
- 文章访问数: 162
- HTML全文浏览量: 87
- PDF下载量: 35
- 被引次数: 0
出版历程
- 收稿日期: 2025-04-11
- 修回日期: 2025-08-06
- 网络出版日期: 2025-08-11

Human-Machine Fusion Intelligent Decision-Making: Concepts, Framework, and Applications

LI Zhe¹,
WANG Ke^{1, 2, 3
, ,},
WANG Biao¹,
ZHAO Ziqi¹,
LI Yafei^{1, 2, 3},
GUO Yibo^{1, 2, 3},
HU Yazhou^{1, 2, 3},
WANG Hua^{1, 2, 3},
LV Pei^{1, 2, 3},
XU Mingliang^{1, 2, 3}

1.
School of Computer and Artificial Intelligence, Zhengzhou University, Zhengzhou 450001, China
2.
National Supercomputing Center in Zhengzhou, Zhengzhou 450001, China
3.
Engineering Research Center of Intelligent Swarm System, Ministry of Education, Zhengzhou 450001, China

Funds: The National Natural Science Foundation of China (62325602, 62036010, 62372416), China Postdoctoral Science Foundation (2020M682348), The Innovation Foundation of Ocean Defense Technology Innovation Center of National Defense of Science Technology and Industry (JJ-2022-709-01), The Natural Science Foundation of Henan Province (242300421215)

摘要

摘要: 人机融合智能是人工智能发展到一定阶段的产物，构成了由弱人工智能向强人工智能过渡的关键中间智能形态。该领域的研究不仅涵盖人工智能基础理论与技术的探索，还涉及人类、机器与环境之间复杂关系的系统性分析。在军事、医疗和驾驶等应用场景中，探索人机融合智能在复杂决策中的应用具有重要的研究意义和实用价值。该文阐述了人机融合智能的概念，分析实现人机融合智能决策的意义；归纳了人机融合智能决策系统的一般框架，并依据决策任务的特性及其中体现的人机关系，总结了人机融合智能决策的3种具体方式，即人类主导型决策、机器主导型决策和人机协同型决策；介绍了人机融合智能决策的典型应用；讨论了人机融合智能决策存在的问题和未来的研究方向。
- 人工智能 /
- 人机融合智能 /
- 智能决策 /
- 人机协同
Abstract: Objective The exponential growth of data volume, advances in computational power, and progress in algorithmic theory have accelerated the development of Artificial Intelligence (AI). Although AI offers unprecedented opportunities across industries, it continues to face limitations such as dependence on large datasets, poor interpretability of learning and decision-making mechanisms, limited robustness, and susceptibility to hallucinations. To overcome these challenges, integrating human cognitive decision-making capabilities and human-like cognitive models into AI systems is essential. This integration gives rise to a new form of intelligence—Human-Machine Fusion Intelligence—which combines physiological and physical characteristics. The core concept is to harness the complementary strengths of humans and machines in information processing and decision-making: humans provide intuitive judgment and contextual understanding, whereas machines are capable of high-speed computation and large-scale data analysis. By establishing a synergistic, collaborative “partnership,” Human-Machine Fusion Intelligent Decision-Making seeks to optimize decision quality through coordinated organic and probabilistic integration. This paradigm holds significant potential to improve decision reliability in mission-critical contexts, such as military operations, medical procedures, and autonomous driving, thus offering both theoretical research value and practical application relevance. Methods This review adopts a systematic research approach to examine Human-Machine Fusion Intelligence in decision-making across four core dimensions. First, it presents a theoretical analysis of the fundamental concepts underpinning Human-Machine Fusion Intelligence and highlights its unique advantages in complex decision-making contexts. Second, it proposes a general framework for Human-Machine Fusion Intelligent Decision-Making systems, emphasizing two key components: situational awareness and collaborative decision-making. Based on this framework, decision-making approaches are categorized into three types according to task characteristics and the nature of human-machine interaction: human-led, machine-led, and human-machine collaborative decision-making. Third, the review synthesizes recent practical advancements in representative application domains. Finally, it examines emerging trends in the development of Human-Machine Fusion Intelligent Decision-Making. Results and Discussions Unlike prior reviews that focus primarily on specific application domains, this article presents a comprehensive overview of Human-Machine Fusion Intelligence across four key dimensions: conceptual foundations, system framework, practical applications, and current challenges and future prospects. The core contributions of this review are summarized in the following four areas: First, it elucidates the advantages of Human-Machine Fusion Intelligent Decision-Making systems: (1) Improved decision-making accuracy—By combining machines’ strengths in data processing and logical reasoning with human capabilities in handling unstructured problems and ethically complex decisions, the system enables dynamic adjustment through a human-in-the-loop mechanism. (2) Enhanced interpretability of decision outcomes—The decision-making process bridges the cognitive gap between humans and machines, providing a transparent, traceable decision path and clarifying accountability boundaries. (3) Greater system robustness—By integrating machines’ risk monitoring and adaptive capabilities with human experiential judgment in complex or uncertain environments, the system establishes a closed-loop collaboration that balances technological rationality with human cognition. Second, the article highlights that Human-Machine Fusion systems cannot operate independently in safety-critical contexts due to imperfect trust mechanisms and ethical constraints. In response, it proposes a hierarchical architecture comprising two key layers: (1) Situational awareness layer, including three core processes: multimodal data perception, cross-modal information fusion, and situational analysis. (2) Collaborative decision-making layer, which distinguishes three decision-making paradigms based on task characteristics and human-machine interaction mode: (a) Human-led decision-making, suited for tasks with high uncertainty and open-ended conditions, where an enhanced intelligence model with a human-in-the-loop is adopted. (b) Machine-led decision-making, appropriate for tasks with lower uncertainty, emphasizing hybrid intelligence through cognitive model integration in automated workflows. (c) Human-machine collaborative decision-making, applicable when human and machine strengths are complementary, allowing for equal, synergistic cooperation to optimize decision efficiency. Third, the article synthesizes recent technological progress, summarizing representative applications of Human-Machine Fusion Intelligent Decision-Making in mission-critical domains such as the military, healthcare, and autonomous driving. Finally, it identifies six key directions for future development: optimization of multimodal perception, fusion of semantic and feature spaces, construction of deep collaborative feedback loops, dynamic task allocation mechanisms, enhancement of system reliability, and development of ethical guidelines. These directions aim to advance efficient collaboration and sustainable evolution of human-machine intelligence. Conclusions Human-Machine Fusion Intelligent Decision-Making offers substantial research value and strong application potential for advancing emerging industries and enabling new intelligent paradigms. Although several exploratory efforts have been made, the field remains in its infancy, lacking a unified and mature theoretical or technological foundation. Key scientific and engineering challenges persist, including the optimization of multimodal perception and data fusion, bridging the semantic gap between human cognition and machine-represented feature spaces, and achieving deep integration of human and machine intelligence. Continued interdisciplinary collaboration will be essential to drive theoretical progress and technological innovation, further unlocking the potential of Human-Machine Fusion Intelligent Decision-Making.
- Artificial Intelligence (AI) /
- Human-machine fusion intelligence /
- Intelligent decision-making /
- Human-machine cooperation

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图 1 人机融合智能的发展历程

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图 2 人机融合智能文献关键词聚类

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图 3 面向新型终端和人机融合场景的NUIX决策系统框架^[45]

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图 4 人机融合智能决策系统框架

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图 5 态势感知的模型架构^[8]

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图 6 JDL数据融合模型架构^[48]

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图 7 BCI系统结构示意图^[56]

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图 8 “超级Nyquist”示意图^[57]

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图 9 主控系统与T-HR3¹

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图 10 多模态表征的结构

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图 11 多模态融合的结构

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图 12 基于人机融合智能的态势认知机制^[77]

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图 13 人类主导型决策系统基本框架

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图 14 人类主导型决策流程图

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图 15 人在回路的混合增强智能^[85]

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图 16 序贯决策的状态转移示意图^[96]

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图 17 人机协同型人机融合智能决策系统基本框架^[112]

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图 18 航母航空保障作业数字孪生实验平台

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图 19 航母舰面半实物增强现实电子沙盘

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图 20 基于人机融合智能的异质多智能体路径规划^[123]

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图 21 舰载机着舰引导感知与决策原型系统^[124]

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图 22 人机融合多智能体作业规划决策框架^[126]

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图 23 基于MPC控制器的脑控车辆系统框^[141]

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表 1 人机融合智能决策方法分类

标准	类型	描述	典型应用场景
人机关系	机主人辅	常用于业务决策问题的求解。关注经济与技术的合理性，外部影响因素较少，确定性因素较多，问题求解流程相对固定，决策过程以机器为主，仅在复杂情境下由人类介入辅助。	资源配置、生产调度等
	人主机辅	常用于战略型决策问题的求解。人类在机器辅助下提出决策问题，明确目标与问题范围，规划求解技术路线，分阶段推进问题解决，并在系统支持下形成决策方案。	服务引擎等
	人机共商	常用于管理类决策问题的求解。此类决策受未来环境中不确定因素影响较大，人类通常具备较深入的研究基础和理论方法，但在关键环节仍需依赖人机协同，以提高决策的科学性与执行效率。	交通规划、移动Agent等
人类角色	人在环外	该类决策由机器独立主导，依托预设规则、算法与模型实现全流程自主运行。人类仅作为旁观者，无需干预系统运作，也无法介入其决策过程。	对话系统、无人车等
	人在环上	人类和机器拥有平等的决策权，人类通过感知外部环境并借助可视化界面实时查看与监督系统反馈，在识别机器难以胜任任务时可及时介入干预，确保决策过程的可靠性与灵活性。	外骨骼机器人、智能假肢等
	人在环内	人类在与机器协同执行任务中处于主导地位，深度参与并主动控制决策过程，依据机器提供的信息进行分析判断，最终决策权与责任由人类承担，机器则作为辅助工具提供支持。	工业机器人、医疗机器人等

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表 2 态势感知层文献总结

模态	文献	算法优势	算法局限	算法输入	算法输出	典型应用场景
多模态数据感知	Gao等人^[51]	引入空间-通道注意力机制的SSD模型，精度高、延迟低，适用于实时检测场景。	依赖高分辨率RGB-D相机，遮挡场景下性能下降，需预训练手势数据集支持。	RGB-D图像	手部边界框坐标及关节位姿估计	空间站机器人遥控操作
	Abdolrahmani等人^[53]	融合语音指令与低功耗蓝牙和超宽带定位，实现高精度室内定位，多模态反馈提升视障用户路径跟随效果。	依赖预部署的室内信标网络，复杂声学环境中语音识别率下降，多用户并行请求时系统响应延迟明显增加。	用户语音指令及定位信号	分层导航指令及触觉振动提示	视障人士室内无障碍导航
	Liu等人^[55]	思维链推理提升诊断准确率，优化诊疗方案降低决策成本，支持实时生成个性化治疗建议。	依赖高质量训练数据，低资源场景下泛化能力较弱，需临床医生复核以防生成错误建议，伦理决策能力有限。	患者病史、实时监测数据及临床指南	个性化治疗建议及置信度评估	肿瘤多学科会诊
跨模态感知融合	An等人^[67]	统一语义映射提升准确率，动态模态加权机制增强低质场景鲁棒性，跨模态推理能力增强。	计算开销较大，跨模态幻觉问题突出，强依赖预训练LLM泛化能力。	多模态数据及领域知识图谱	跨模态语义向量及自然语言决策	智能客服
	Trende等人^[74]	融合EEG与肌电信号提升转向意图识别准确率，增量上下文整合机制缩短决策延时，动态博弈模型增强路口适应性。	依赖高密度脑电帽，强光照或颠簸路况下信号噪声显著增加，个体脑电特征差异需预校准。	EEG脑电信号、车辆状态及环境拓扑	转向意图分类及置信度	智能汽车人机共驾控制
	Ghosh等人^[76]	非语音音频识别精度提升，复杂场景推理能力增强，指令跟随响应延迟降低。	多模态同步依赖强，小样本泛化能力弱，计算负载较高。	原始音频波形、文本指令及环境上下文	自然语言语义描述、推理结论及决策建议	智能家居环境感知
态势分析	王玉虎等人^[77]	融合人类智慧与机器智能提升态势理解准确率，动态任务分配机制缩短响应延迟，多源异构数据融合效率提升。	依赖高质量人类标注数据，突发态势下认知体协作效率较低，个体决策偏好建模需预校准。	多源战场情报、专家经验规则及环境拓扑	态势认知3元组及动态决策建议	军事指挥决策支持、应急响应系统
	Huang等人^[78]	自动化降级检测延迟，故障响应速度提升，模糊规则与强化学习融合的动态分配模型降低人机冲突率。	依赖高精度传感器实时监测，疲劳驾驶员控制权移交失败率升高，多源异构数据融合计算负载较大。	车辆状态、环境感知数据及自动化系统健康指标	动态控制权分配系数及降级补偿指令	自动驾驶系统降级应急
	Huang等人^[79]	人因工程优化降低职业灾害风险，虚拟仿真平台提升生产线布局效率，动态任务分配模型增强人机协作适应性。	依赖高精度传感器网络，部署成本较高，跨部门数据孤岛阻碍系统集成。	多源生产数据及环境参数	人机任务分配矩阵及动态调度策略	汽车混线生产调度

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表 3 协同决策层文献总结

模态	文献	算法优势	算法局限	算法输入	算法输出	典型应用场景
人类主导型决策	Zhou等人^[83]	HCPS框架实现跨层级动态优化决策，人机混合增强智能提升复杂工艺参数优化效率。	跨行业通用性验证不足，中小企业数据基础设施薄弱制约实施，认知模块依赖高质量标注数据。	多源异构制造数据及人类专家知识规则。	全生命周期优化决策	汽车柔性生产线动态调度
	Nikitin等人^[84]	图神经网络融合设备拓扑关系提升维护优先级排序准确率，人在回路主动学习机制降低标注成本。	依赖高质量设备关联图谱与人类专家反馈，决策时效性降低，跨行业异构系统迁移需重新训练图模型。	多源设备传感器时序数据、维护历史及专家规则标注。	维护优先级评分、动态策略建议	电网设备集群预测性维护
	Shi等人^[87]	HuMAL算法通过人类反馈自适应学习个人价值观，道德显著样本选择机制减少标注需求。	依赖文本游戏简化环境，价值观冲突场景泛化能力有限，人类反馈延迟时决策质量下降。	文本游戏状态描述及人类道德评分反馈。	道德对齐的动作决策、个人价值观策略	人工智能伦理教育系统
机器主导型决策	Liu等人^[91]	DDM量化驾驶员决策中的认知参数，蒙特卡洛仿真用于精准预测危险行为发生频率。	依赖高精度车辆轨迹数据，未整合个体差异，实时应用存在计算延迟。	车辆瞬时速度及信号相位时序。	决策行为分类、危险行为概率	交叉口信号时序优化
	Huang等人^[94]	条件运动预测提升交互场景预测精度，IRL学习成本函数提升轨迹人类相似度，多模态候选轨迹生成支持动态博弈决策。	候选轨迹数量有限，预测模块依赖高精度地图标注，实时计算存在延迟。	多帧RGB图像、激光雷达点云及历史序列特征。	鸟瞰图空间3D 检测框	L4级自动驾驶全栈感知
	Robertazzi 等人^[97]	神经调节驱动的元学习框架动态调整超参数，双记忆系统模拟人脑巩固机制。	依赖预定义的神经递质动态规则，复杂多任务泛化能力未验证，需模拟“睡眠”巩固阶段，实时性有限。	冲突决策任务信号	动作抑制指令	驾驶紧急制动
	Li等人^[99]	博弈交互模块提升行人轨迹预测精度，安全约束机制降低危险行为，多模态生成能力支持个性化行人策略生成。	依赖高精度运动捕捉数据，复杂天气下传感器噪声未建模，实时计算延迟较高。	行人姿态序列、车辆运动状态及环境拓扑。	行人未来轨迹分布及交互行为分类	自动驾驶行人避撞测试
人机协同型决策	Zhao等人^[109]	MAToM-DM实现心理揣测，MAToM-SNN模拟前额叶皮层机制，冲突决策缩短延迟。	依赖预定义神经递质规则，复杂多任务泛化能力未验证，脉冲编码生物合理性牺牲实时性。	多智能体观测状态及任务目标信号。	协同策略指令	多机器人协同搜救
	Feng等人^[110]	RL策略模型动态优化人机协作时机，降低人类干预成本。	依赖高质量人机协作预训练数据集，实时环境动态适应性较弱，人类决策偏好建模需个性化校准。	任务状态描述、历史决策序列及环境反馈信号。	协作策略及任务执行动作	医疗诊断决策支持
	Liu等人^[111]	柔性时间窗调度降低乘客期望偏差，人机协同优化缩减车队规模，帕累托解集可视化提升决策效率。	依赖高质量乘客需求数据，突发路况扰动适应性较弱，多目标优化求解复杂度较高。	乘客起讫点数据、时间窗约束、车辆容量及路网拓扑。	最优发车时刻表及车辆-乘客匹配方案	城市需求响应公交

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表 4 军事领域的人机融合智能决策系统

平台	用途	特点	研发机构
DARPA’s Insight³	数据和情报分析平台	通过整合多源数据，结合高级算法与机器学习技术，实现实时情报分析与人机融合决策支持，提升系统的智能响应能力与协同决策水平。	英国宇航系统公司
Palantir Gotham³	数据整合、分析和可视化平台	具备强大的数据处理能力，能够借助直观的可视化界面揭示数据中的潜在模式与关联关系，从而有效辅助用户做出更精准和高效的决策。	帕兰提尔科技公司
Joint Automated Deep Operations Coordination System³	联合作战指挥	通过集成多源数据，实现实时战场态势感知与人机融合决策支持，助力指挥官开展深度作战协调，提升快速响应与动态战术调整能力。	美国国防部
Advanced Field Artillery Tactical Data System³	野战炮兵作战指挥系统	具备快速计算与协调多种火力支援任务的能力，能够将实时数据反馈至指挥官，显著提升作战效率与打击精准度。	莱多斯控股公司
Aegis Combat System³	舰载综合作战系统	通过集成先进传感器与武器控制技术，实现对敌方飞机、导弹及舰船目标的自动探测、跟踪与摧毁，具备全面防御与攻击能力，并为指挥官提供高效决策支持。	洛克希德·马丁航天系统公司
Command and Control, Battle Management, and Communications³	导弹防御系统	具备实时态势感知、战术规划与人机融合智能决策支持能力，保障导弹防御系统高效运行与精准响应。	洛克希德·马丁航天系统公司
Net-Centric Enterprise Services³	防务信息共享与服务平台	通过整合多源信息系统与数据，构建统一操作界面与人机融合决策支持工具，显著提升决策效率与操作便捷性。	美国国防部
Command Post of the Future³	指挥和控制决策支持系统	提供实时战术信息与可视化工具，支持指挥官与决策者在虚拟环境中协同规划作业，提升指挥效率与人机融合决策质量。	通用动力任务系统公司

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