TY - JOUR
T1 - Multiagent Decision-Making Dynamics Inspired by Honeybees
AU - Gray, Rebecca
AU - Franci, Alessio
AU - Srivastava, Vaibhav
AU - Leonard, Naomi Ehrich
N1 - Funding Information:
Manuscript received November 21, 2017; accepted December 4, 2017. Date of publication January 23, 2018; date of current version June 18, 2018. This work was supported in part by the NSF under Grant CMMI-1635056, in part by ONR under Grant N00014-14-1-0635, and in part by DGAPA-PAPIIT (UNAM) under Grant IA105816. Recommended by Associate Editor J. Baillieul. R. Gray and A. Franci share first authorship of this paper. (Corresponding author: Naomi Ehrich Leonard.) R. Gray and N. E. Leonard are with the Department of Mechanical and Aerospace Engineering, Princeton University, Princeton, NJ 08544 USA (e-mail: rgray@princeton.edu; naomi@princeton.edu).
Publisher Copyright:
© 2014 IEEE.
PY - 2018/6
Y1 - 2018/6
N2 - 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.
AB - 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.
KW - Adaptive control
KW - animal behavior
KW - bifurcation
KW - decentralized control
KW - decision-making
KW - multiagent systems
KW - networked control systems
KW - nonlinear dynamical systems
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U2 - 10.1109/TCNS.2018.2796301
DO - 10.1109/TCNS.2018.2796301
M3 - Article
AN - SCOPUS:85040935535
SN - 2325-5870
VL - 5
SP - 793
EP - 806
JO - IEEE Transactions on Control of Network Systems
JF - IEEE Transactions on Control of Network Systems
IS - 2
ER -