Multi-agent decision-making dynamics inspired by honeybees

Rebecca Gray, Alessio Franci, Vaibhav Srivastava and Naomi Ehrich Leonard

IEEE Transactions on Control of Network Systems, 2018.

arXiv:1711.11578v1 [math.OC] November 2017.

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arXiv:1711.11578v1 [math.OC]
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 decisionmaking. 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 decisionmaking 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 decisionmaking performance beyond that observed in swarms.

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