Multiagent Decision-Making Dynamics Inspired by Honeybees

• Published in: IEEE transactions on control of network systems 2018-06, Vol.5 (2), p.793-806
• Main Authors: Gray, Rebecca, Franci, Alessio, Srivastava, Vaibhav, Ehrich Leonard, Naomi
• Format: Article
• Language: English
• Subjects: Adaptation models; Adaptive control; Analytical models; animal behavior; Animals; Bifurcation; Bifurcation control; Biological system modeling; Computer Networks and Communications; Control and Optimization; Control and Systems Engineering; decentralized control; Decision making; Engineering, computing & technology; Ingénierie, informatique & technologie; Multi-agent decision making; Multiagent networks; multiagent systems; networked control systems; nonlinear dynamical systems; Pitch-fork bifurcations; Robustness; Signal Processing
• ISSN: 2325-5870; 2325-5870; 2372-2533
• DOI: 10.1109/TCNS.2018.2796301

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 decisionmaking 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.