  An emerging challenge in swarm shepherding research is to design effective
and efficient artificial intelligence algorithms that maintain a
low-computational ceiling while increasing the swarm's abilities to operate in
diverse contexts. We propose a methodology to design a context-aware
swarm-control intelligent agent. The intelligent control agent (shepherd) first
uses swarm metrics to recognise the type of swarm it interacts with to then
select a suitable parameterisation from its behavioural library for that
particular swarm type. The design principle of our methodology is to increase
the situation awareness (i.e. information contents) of the control agent
without sacrificing the low-computational cost necessary for efficient swarm
control. We demonstrate successful shepherding in both homogeneous and
heterogeneous swarms.
