  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.
