# Onto4MAT: A Swarm Shepherding Ontology for Generalised Multi-Agent Teaming

arXiv: https://arxiv.org/abs/2203.12955 · HTML: https://mlsanity.com/paper/2203.12955/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- Builds on 4 of its 73 references that are in the map.
- First posted on arXiv 24 Mar 2022.

## Builds on (most cited first, 4 of 4)

- [SOSA: A Lightweight Ontology for Sensors, Observations, Samples, and Actuators](https://mlsanity.com/paper/1805.09979/) (2018)
- [A Comprehensive Review of Shepherding as a Bio-inspired Swarm-Robotics Guidance Approach](https://mlsanity.com/paper/1912.07796/) (2019)
- [Anatomy of Leadership in Collective Behaviour](https://mlsanity.com/paper/1802.01194/) (2018)
- [Towards Bi-Directional Communication in Human-Swarm Teaming: A Survey](https://mlsanity.com/paper/1803.03093/) (2018)

## Built on by (most cited first, 4 of 4)

- [Swarm Analytics: Designing Information Markers to Characterise Swarm Systems in Shepherding Contexts](https://mlsanity.com/paper/2208.12386/) (2022)
- [Contextually Aware Intelligent Control Agents for Heterogeneous Swarms](https://mlsanity.com/paper/2211.12560/) (2022)
- [SymboSLAM: Semantic Map Generation in a Multi-Agent System](https://mlsanity.com/paper/2403.15504/) (2024)
- [Advancing Frontiers in SLAM: A Survey of Symbolic Representation and Human-Machine Teaming in Environmental Mapping](https://mlsanity.com/paper/2405.01398/) (2024)

## Abstract (arXiv)

Research in multi-agent teaming has increased substantially over recent years, with knowledge-based systems to support teaming processes typically focused on delivering functional (communicative) solutions for a team to act meaningfully in response to direction. Enabling humans to effectively interact and team with a swarm of autonomous cognitive agents is an open research challenge in Human-Swarm Teaming research, partially due to the focus on developing the enabling architectures to support these systems. Typically, bi-directional transparency and shared semantic understanding between agents has not prioritised a designed mechanism in Human-Swarm Teaming, potentially limiting how a human and a swarm team can share understanding and informationtextemdash data through concepts and contextstextemdash to achieve a goal. To address this, we provide a formal knowledge representation design that enables the swarm Artificial Intelligence to reason about its environment and system, ultimately achieving a shared goal. We propose the Ontology for Generalised Multi-Agent Teaming, Onto4MAT, to enable more effective teaming between humans and teams through the biologically-inspired approach of shepherding.