English (United Kingdom)

https://curated-unify.zendy.io/wp-json/zendy-region/v1/featured_content/oa?rat=en

https://curated-unify.zendy.io/wp-json/zendy-region/v1/highlighted_journal/

Global: Zendy Plus annual

Presents the access of premium content as premium feature

Premium Content

Presents the keyphrase highlighting as premium feature

Keyphrase Highlighting

Presents the summarisation as premium feature

Summarisation

Insights

Presents the pdf analysis as premium feature

PDF Analysis

Presents the zaia usage as premium feature

ZAIA

Global: Zendy Tools annual

Global: Zendy Free Plan

Global: Zendy Tools monthly

Global: Zendy Plus monthly

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 primarily relied on knowledge-based systems, with rules triggered based on the mode of interaction. Enabling humans to join the team of AI agents effectively 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. We present an ontology designed specifically for human-agent teaming to address this research gap. The ontology is general, but we then contextualise it into a particular swarm-shepherding scenario to illustrate its use in a particular context. The proposed Ontology for Generalised Multi-Agent Teaming <italic>Onto4MAT</italic> offers the underlying building blocks for effective communication and shared understanding between humans and multi-agent teams.

Onto4MAT: A Swarm Shepherding Ontology for Generalized Multiagent Teaming