Onto4MAT: A Swarm Shepherding Ontology for Generalized Multiagent Teaming
Adam Hepworth, Daniel P. Baxter, Hussein A. Abbass · IEEE Access · 2022
Research in multi-agent teaming has increased substantially over recent years. Underneath these attempts sits a suite of communication functions to enable effective teaming. The Artificial Intelligence (AI) systems supporting the teaming arrangement have mostly relied on knowledge-based systems, with a set of rules triggered based on mode of interaction. Enabling humans to effectively join the team of AI agents calls for both the humans and AI agents to share their understanding and representation of their shared worlds. Such shared understanding requires formal representations of concepts to support transparency during bi-directional communications between team members. Little research has been done in this space, especially when humans need to team with a swarm of agents. To address this research gap, we present an ontology designed specifically for human-agent teaming. The ontology is general, but we then contextualise it into a particular swarm-shepherding scenario to illustrate how it can be used in a particular context. The proposed Ontology for Generalised Multi-Agent Teaming Onto4MAT offers the underlying building blocks for effective communication and shared understanding between humans and multi-agent teams.