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Contemporary swarm indicators are often used in isolation, focussed on extracting information at the individual or collective levels. Consequently, these are seldom integrated to infer a top-level operating picture of the swarm, its members and its overall collective dynamics. The primary contribution of this paper is to organise a suite of indicators about swarms into an ontologically arranged collection of information markers to characterise the swarm from the perspective of an external observer – , a recognition agent. Our contribution shows the foundations for a new area of research that we title swarm analytics, whose primary concern is with the design and organisation of collections of swarm markers to understand, detect, recognise, track and learn a particular insight about a swarm system. We present our designed framework of information markers that offer a new avenue for swarm research, especially for heterogeneous and cognitive swarms that may require more advanced capabilities to detect agencies and categorise agent influences and responses.

Boids (bird-oids) is a widely used model to mimic the behaviour of birds. Shoids (sheep-oids) rely on the same boids rules with the addition of a repulsive force away from a sheepdog (a herding agent). Previous work assumed homogeneous shoids. Real-world observations of sheep show non-homogeneous responses to the presence of a herding agent. We present a portfolio of information-theoretic and spatial indicators to track the footprints of shoids with different parameters within the shoid flock. The portfolio is named the Centre of Influence to indicate that the aim is to identify the influential shoids with the highest impact on flock dynamics. We use both synthetic simulation-driven data and measurements collected from live sheep herding trials by an unmanned aerial vehicle (UAV) to validate the proposed measures. The resultant measures will allow us in our future research to design more efficient control strategies for the UAV, by polarising the attention of the machine learning algorithm on those shoids with influence footprints, to drive the flock to improve the herding of sheep.

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