Adaptive Equalized Multigroup Role Assignment in Ordered Subtasks
针对现有方法忽略子任务依赖和重要性差异的问题，基于E-CARGO模型提出自适应均衡多组角色分配方法，通过均衡各组性能并允许容忍偏差来高效求解，实验证明其稳定性和有效性。
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.