Contextually Aware Intelligent Control Agents for Heterogeneous Swarms
This addresses the problem of efficient swarm control for robotics or AI applications, but it appears incremental as it builds on existing shepherding methods with context-awareness.
The paper tackled the challenge of designing low-computational AI algorithms for swarm shepherding by proposing a context-aware control agent that uses swarm metrics to select behavioral parameters, demonstrating successful shepherding in homogeneous and heterogeneous swarms.
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.