Multiagent Decision-Making Dynamics Inspired by Honeybees

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

Research output: Contribution to journalArticle

19 Scopus citations

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 languageEnglish (US)
Pages (from-to)793-806
Number of pages14
JournalIEEE Transactions on Control of Network Systems
Volume5
Issue number2
DOIs
StatePublished - 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

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