Role-Based Human-Machine Collaboration Task-Allocation Strategy in Multiagent Environment

The human–machine collaboration task-allocation problem involves three major challenges: role diversity, capability heterogeneity, and task dynamics. Most existing studies treat humans and machines as parallel units through a static “Human + Machine” additive paradigm, which neglects the evolution of capability during collaboration. Some works “deeply couple” relatively low-autonomy machines with humans, thereby limiting the system’s flexibility in resource scheduling and dynamic reconfiguration. This study analyzes the problem from a multiagent system perspective and classifies execution units into three types: human agents, machine agents, and human–machine collaborative agents, and distinguishes between their independent and collaborative capabilities. Next, we propose the dynamic short-board balance synergy assessment method, which integrates the “short-board” concept to quantify collaboration performance and leverages agents that have low independent but high collaborative capabilities. By incorporating multiple constraints, we establish the role-based human–machine collaboration (RBHMC) model, prove its NP-hardness, and design a multi-level solving approach to handle small-scale and medium-to-large-scale data separately. The experimental results indicate that, compared with “Human + Machine” and “Deep Coupling” models, RBHMC outperforms in task completion rate, resource utilization, and system robustness. An industrial case study further validates its applicability and superiority in real-world settings. Finally, RBHMC’s transferability is validated through vertical technology adaptation and horizontal scenario migration, providing a scalable solution for multidomain human–machine collaboration in complex scenarios.