The design of distributed autonomous systems often omits consideration of the underlying network dynamics. Recent works in multi-agent systems and swarm robotics alike have highlighted the impact that the interactions between agents have on the collective behaviours exhibited by the system. In this paper, we seek to highlight the role that the underlying interaction network plays in determining the performance of the collective behaviour of a system, comparing its impact with that of the physical network. We contextualise this by defining a collective learning problem in which agents must reach a consensus about their environment in the presence of noisy information. We show that the physical connectivity of the agents plays a less important role than when an interaction network of limited connectivity is imposed on the system to constrain agent communication. Constraining agent interactions in this way drastically improves the performance of the system in a collective learning context. Additionally, we provide further evidence for the idea that `less is more' when it comes to propagating information in distributed autonomous systems for the purpose of collective learning.

翻译：分布式自主系统的设计常忽略底层网络动力学。近期针对多智能体系统与群体机器人的研究均强调了智能体间交互对系统集体行为表现的影响。本文旨在阐明底层交互网络在决定系统集体行为性能中的作用，并将其与物理网络的影响进行比较。我们通过定义一个集体学习问题来具体阐述该问题——在该问题中，智能体需在含有噪声的信息环境下对环境达成共识。研究表明，相比于对系统施加有限连通性的交互网络以约束智能体通信，智能体的物理连通性对系统表现的影响较小。通过这种方式约束智能体交互，能显著提升系统在集体学习情境中的性能。此外，我们进一步验证了分布式自主系统中为集体学习目的传播信息时"少即是多"的观点。