Multi-agent decision-making dynamics inspired by honeybees

TL;DR

This paper models honeybee swarm decision-making dynamics to develop bio-inspired multi-agent network systems that are robust, adaptive, and capable of efficient value-sensitive decisions, with applications in control systems.

Contribution

It introduces a novel distributed multi-agent decision-making model inspired by honeybees, incorporating bifurcation control to improve network performance.

Findings

The model exhibits a pitchfork bifurcation similar to biological decision processes.

The bio-inspired dynamics achieve high-value decision accuracy.

The adaptive bifurcation control law enhances decision-making robustness.

Abstract

When choosing between candidate nest sites, a honeybee swarm reliably chooses the most valuable site and even when faced with the choice between near-equal value sites, it makes highly efficient decisions. Value-sensitive decision-making is enabled by a distributed social effort among the honeybees, and it leads to decision-making dynamics of the swarm that are remarkably robust to perturbation and adaptive to change. To explore and generalize these features to other networks, we design distributed multi-agent network dynamics that exhibit a pitchfork bifurcation, ubiquitous in biological models of decision-making. Using tools of nonlinear dynamics we show how the designed agent-based dynamics recover the high performing value-sensitive decision-making of the honeybees and rigorously connect investigation of mechanisms of animal group decision-making to systematic, bio-inspired control of multi-agent network systems. We further present a distributed adaptive bifurcation control law and prove how it enhances the network decision-making performance beyond that observed in swarms.