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.
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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