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

LI Zhe; WANG Ke; WANG Biao; ZHAO Ziqi; LI Yafei; GUO Yibo; HU Yazhou; WANG Hua; LV Pei; XU Mingliang

doi:10.11999/JEIT250260

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LI Zhe, WANG Ke, WANG Biao, ZHAO Ziqi, LI Yafei, GUO Yibo, HU Yazhou, WANG Hua, LV Pei, XU Mingliang. Human-Machine Fusion Intelligent Decision-Making: Concepts, Framework, and Applications[J]. Journal of Electronics & Information Technology. doi: 10.11999/JEIT250260

Citation:

LI Zhe, WANG Ke, WANG Biao, ZHAO Ziqi, LI Yafei, GUO Yibo, HU Yazhou, WANG Hua, LV Pei, XU Mingliang. Human-Machine Fusion Intelligent Decision-Making: Concepts, Framework, and Applications[J]. Journal of Electronics & Information Technology. doi: 10.11999/JEIT250260

Citation:

LI Zhe, WANG Ke, WANG Biao, ZHAO Ziqi, LI Yafei, GUO Yibo, HU Yazhou, WANG Hua, LV Pei, XU Mingliang. Human-Machine Fusion Intelligent Decision-Making: Concepts, Framework, and Applications[J]. Journal of Electronics & Information Technology. doi: 10.11999/JEIT250260

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

doi: 10.11999/JEIT250260 cstr: 32379.14.JEIT250260

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)

Received Date: 2025-04-11
Rev Recd Date: 2025-08-06

Available Online: 2025-08-11

Abstract

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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