Role-Based Human-Machine Collaboration Task-Allocation Strategy in Multiagent Environment
Why this work is in the frame
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Bibliographic record
Abstract
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.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it