Onto4MAT: A Swarm Shepherding Ontology for Generalized Multiagent Teaming

Hepworth, Adam J.; Baxter, Daniel P.; Abbass, Hussein A.

doi:10.1109/access.2022.3180032

Cited by 3 publications

(2 citation statements)

References 62 publications

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“…Context can be defined as any information that can be used to characterise the situation of an entity relevant to the task at hand. In shepherding, context could be defined by characteristics of the control agent, swarm agents, environment, and user priorities Hepworth, Baxter, and Abbass (2022). Context-sensitive systems "are those that respond to changes in their environment" (pg.306) Abowd et al (1999) by utilising context awareness to optimise performance in non-stationary environments.…”

Section: Context-awarenessmentioning

confidence: 99%

Contextually Aware Intelligent Control Agents for Heterogeneous Swarms

Hepworth

Hussein

Reid

et al. 2022

Preprint

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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.

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Section: Context-awarenessmentioning

confidence: 99%

Contextually Aware Intelligent Control Agents for Heterogeneous Swarms

Hepworth

Hussein

Reid

et al. 2022

Preprint

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“…The second challenge motivated a line of research on activity recognition of human-swarm interaction (Hepworth, 2021), as well as designing ontologies to represent the space of concepts lying between humans and the swarm (Abbass & Hunjeta, 2021a; Baxter et al, 2021; Hepworth et al, 2022), contributing to a holistic theory to inform how humans and swarm should interact (Hasbach & Bennewitz, 2021). The third challenge motivated the design of contextual indicators that could be extracted from the sensorial information to guide the swarm.…”

Section: Introductionmentioning

confidence: 99%

Swarm analytics: Designing information markers to characterise swarm systems in shepherding contexts

et al. 2022

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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.

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