Group Role Assignment with a Training Plan Considering the Duration in Adaptive Collaboration

Abstract

Group Role Assignment (GRA) seeks the maximum group performance by assigning roles to the most appropriate individual agents. As an extension of GRA, GRA with a Training Plan (GRATP) is proposed to address the trainingrelated collaboration problems. However, the existing models neglect the influence of changes in duration, i.e., the duration of training is a preset constant, which has limited applications. The variation of duration has a great influence on the improvement of the agent’s ability and assignment, especially in the dynamic scenario. Therefore, this paper formalizes a GRATP problem that considers the duration in dynamic scenarios, and constructs an algorithm to solve it. In the formulated problem, the training plan includes three factors: the starting time, the duration and the training programs. The total benefit is taken as the goal of the algorithm, rather than the group performance, because the agent’s ability and group performance vary over time in adaptive collaboration (AC). Moreover, the reassignment needs to be launched after training, as the original assignment may not be optimal. By utilizing GRA and its Environment—Classes, Agents, Roles, Groups, and Objects (E-CARGO) model, the optimal training plan and reassignment are obtained by maximizing the total benefit. Experiments verify the effectiveness of the proposed algorithm.