The Impact of Network Connectivity on Collective Learning

Abstract

The design of distributed autonomous systems often omits consideration of the underlying network dynamics. Recent works in multiagent 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.

Imprecise Evidence in Social Learning

“…Another avenue of future research will be to add communication constraints to the model, such as network connectivity or physical distance range. There are recent studies showing that limited connectivity can improve the performance of social learning (Crosscombe and Lawry, 2021) and that constrained communication of multi-agent systems can be more robust to the environment changes (Talamali et al, 2021).…”

“…We consider social learning in terms of two distinct processes; evidential updating and belief fusion (Crosscombe and Lawry, 2021). The former is the the process by which the robots learn directly from the environment, by updating their current beliefs based on evidence received from the environment.…”

Imprecise Evidence in Social Learning

“…Another avenue of future research will be to add communication constraints to the model, such as network connectivity or physical distance range. There are recent studies showing that limited connectivity can improve the performance of social learning (Crosscombe and Lawry, 2021) and that constrained communication of multi-agent systems can be more robust to the environment changes (Talamali et al, 2021). Additionally, it's worth noting that different types of network connectivity may have varied performance outcomes.…”