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 multiagent 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 an investigation of mechanisms of animal group decision-making to systematic, bioinspired control of multiagent 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.
Original language | English (US) |
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Pages (from-to) | 793-806 |
Number of pages | 14 |
Journal | IEEE Transactions on Control of Network Systems |
Volume | 5 |
Issue number | 2 |
DOIs | |
State | Published - Jun 2018 |
All Science Journal Classification (ASJC) codes
- Control and Systems Engineering
- Signal Processing
- Computer Networks and Communications
- Control and Optimization
Keywords
- Adaptive control
- animal behavior
- bifurcation
- decentralized control
- decision-making
- multiagent systems
- networked control systems
- nonlinear dynamical systems