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
information\textemdash data through concepts and contexts\textemdash 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.

本篇研究提出了 Onto4MAT，这是一种形式化的知识表示设计，通过牧羊的生物学方法，促进了人类和团队之间更有效的协作。

Onto4MAT: 用于广义多智能体团队协作的群体管理本体

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

Multi-agent teaming achieves better performance when there is communication
among participating agents allowing them to coordinate their actions for
maximizing shared utility. However, when collaborating a team of agents with
different action and observation spaces, information sharing is not
straightforward and requires customized communication protocols, depending on
sender and receiver types. Without properly modeling such heterogeneity in
agents, communication becomes less helpful and could even deteriorate the
multi-agent cooperation performance. We propose heterogeneous graph attention
networks, called HetNet, to learn efficient and diverse communication models
for coordinating heterogeneous agents towards accomplishing tasks that are of
collaborative nature. We propose a Multi-Agent Heterogeneous Actor-Critic
(MAHAC) learning paradigm to obtain collaborative per-class policies and
effective communication protocols for composite robot teams. Our proposed
framework is evaluated against multiple baselines in a complex environment in
which agents of different types must communicate and cooperate to satisfy the
objectives. Experimental results show that HetNet outperforms the baselines in
learning sophisticated multi-agent communication protocols by achieving
$\sim$10\% improvements in performance metrics.

该研究提出了一种名为 HetNet 的异构网络注意力思想，用于协调不同类型的机器人团队完成合作任务，并评估 HetNet 在解决信息交互问题中的性能提升，实验结果表明其相比基准方法，在学习复杂多智能体协作的通信协议方面，表现更好。