Biologically Inspired Effectiveness Measures for Shepherding-Based guidance Systems

We present quantitative metrics to assess the effectiveness of shepherding-based human-swarm guidance. Our swarm is flocks of sheep. Our human-swarm teaming arrangement contrasts a sheepdog and a human shepherd trained using traditional methods, and a drone and a human pilot trained using computational models of abstracted mustering skills. We design two effectiveness attributes for mustering that measure the level of influence a sheepdog/drone exerts on a sheep (swarm-guidance-controllability) and the level of anticipation that assists sheep to modulate their response (swarm-guidance-predictability). We collect field data where the sheep are mustered by a sheepdog in one setting and by a drone in another. We contrast field data against data collected from simulation models. Our analysis reveals that while collective behaviours in simulations may appear to be similar to field data, the dynamics of influence are vastly different between the two. The findings are particularly important when assessing the suitability of contemporary swarm shepherding and collective behaviour models produced by the research community.