We aim to improve the performance of multi-agent flocking behavior by quantifying the structural significance of each agent. We designed a confidence score(ConfScore) to measure the spatial significance of each agent. The score will be used by an auxiliary controller to refine the velocity of agents. The agents will be enforced to follow the motion of the leader agents whose ConfScores are high. We demonstrate the efficacy of the auxiliary controller by applying it to several existing algorithms including learning-based and non-learning-based methods. Furthermore, we examined how the auxiliary controller can help improve the performance under different settings of communication radius, number of agents and maximum initial velocity.

翻译：我们的目标是通过量化每个代理商的结构意义来改进多试剂群集行为的性能。 我们设计了一个信任评分(ConfScore)来衡量每个代理商的空间意义。 该评分将由辅助控制员用来改进代理商的速度。 代理商将被强制跟踪其分数高的领导代理商的动作。 我们通过将其应用到包括学习和非学习方法在内的若干现有算法中来证明辅助控制员的效力。 此外, 我们考察了辅助控制员如何在通信半径、代理人数量和最大初始速度的不同环境下帮助改进性能。