Contextually Aware Intelligent Control Agents for Heterogeneous Swarms

TL;DR

This paper introduces a low-computational, context-aware control agent for heterogeneous swarms that adapts its behavior based on swarm metrics to improve effectiveness across diverse scenarios.

Contribution

It presents a novel methodology for designing intelligent agents that recognize swarm types and adapt their control parameters accordingly, enhancing swarm management.

Findings

Successful shepherding in homogeneous swarms

Effective control in heterogeneous swarm scenarios

Maintains low computational overhead

Abstract

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.