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Adaptive Equalized Multigroup Role Assignment in Ordered Subtasks
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AI
DOI：10.1109/TSMC.2024.3390138.png)
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Abstract
En 中文
Role-based collaboration (RBC) is a new problem-solving paradigm that uses model environments-classes, agents, roles, groups, and objects (E-CARGO) to facilitate modeling. Task decomposition is widely adopted to reduce the difficulty of execution, resulting in multigroup collaboration problems. Multigroup role assignment (MGRA) has been proposed to solve the assignment of multiple groups. In many actual scenarios, there are dependencies between the decomposed subtasks, which is neglected by existing MGRA methods. Moreover, they disregard the fact that subtasks have diverse significance in the development of a project, which is of paramount importance to ensure the proper allocation of resources. To solve the complicated problem, the structured subtasks are formalized based on the emerging and promising RBC theory and E-CARGO model. Then, the assignment is abstracted into a complicated single-objective multiconstraint problem, named adaptive-equalized MGRA (AE-MGRA). In the formulated AE-MGRA problem, to improve the utilization of limited resources, the performance of each E-CARGO group needs to be equalized according to the corresponding subtask's weight. As the optimal solution is difficult and time consuming to obtain, a tolerable deviation is utilized to achieve a near-optimal solution. Extensive experiments are conducted to sufficiently demonstrate the efficiency and stability of the proposed practical solution. In addition, the experimental results on static assignment and dynamic assignment further prove the effectiveness of the solution.
Keywords:
Adaptive-equalized multigroup role assignment (AE-MGRA)
environments-classes
agents
roles
groups
and objects (E-CARGO)
role-based collaboration (RBC)
subtask
Journal
IF:
10.5
Papers:
1.1W
Citations:
5.0W
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