arrow
Return

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

delete2025-08-01
delete0
PRE
AI
X
Xinlei Zhang
Z
Zhaoquan Zhu
Y
Yuanbai Li
H
Haibin Zhu
孙
孙宇祥 (Yuxiang Sun) *
X
Xianzhong Zhou *
DOI:10.1109/THMS.2025.3591603delete
deleteOriginal
deleteOriginal request for help
deleteShare
deleteSave
Abstract

Abstract

En 中文
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.
Keywords:
Collaboration
Adaptation models
Resource management
Multi-agent systems
Artificial intelligence
Couplings
Computational modeling
Companies
Optimization
Human-machine systems
Human-machine collaboration (HMC)
multiagent systems
role-based collaboration (RBC)
task allocation

Journal

IEEE Transactions on Human-Machine Systems cover
IEEE Transactions on Human-Machine Systems
IF:
4.4
Papers:
1.1K
Citations:
3.5K

Organization

N
nipissing university
Scholars:
16
Papers: 19
Citations: 0
N
Nanjing University
Scholars:
7.0K
Papers: 2.6K
Citations: 8.1W
Cited Papers

Cited Papers

Optimal energy management and control aspects of distributed microgrid using multi-agent systems
err2019-01-01
err116
PREAI
errKhan, Muhammad Waseem; Wang, Jie; Ma, Meiling; Xiong, Linyun; Li, Penghan; Wu, Fei
errShare
errSave
Decentralized Task Allocation in Multi-Agent Systems Using a Decentralized Genetic Algorithm
err2020-05-01
err0
PREAI
errRuchir Patel; Eliot Rudnick-Cohen; Shapour Azarm; Michael Otte; Huan Xu; Jeffrey W. Herrmann
errShare
errSave
E‐CARGO and Role‐Based Collaboration
err
IF0
err2021-11-19
err0
PREAI
errHaibin Zhu
errShare
errSave
researcher View more