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An emerging challenge in swarm shepherding research is to design effectiveand efficient artificial intelligence algorithms that maintain alow-computational ceiling while increasing the swarm's abilities to operate indiverse contexts. We propose a methodology to design a context-awareswarm-control intelligent agent. The intelligent control agent (shepherd) firstuses swarm metrics to recognise the type of swarm it interacts with to thenselect a suitable parameterisation from its behavioural library for thatparticular swarm type. The design principle of our methodology is to increasethe situation awareness (i.e. information contents) of the control agentwithout sacrificing the low-computational cost necessary for efficient swarmcontrol. We demonstrate successful shepherding in both homogeneous andheterogeneous swarms.

Contextually Aware Intelligent Control Agents for Heterogeneous Swarms