Adaptive control; animal behavior; bifurcation; decentralized control; decision-making; multiagent systems; networked control systems; nonlinear dynamical systems; Adaptation models; Adaptive Control; Animal behavior; Bifurcation control; Biological system modeling; Multi-agent decision making; Multiagent networks; Pitch-fork bifurcations; Control and Systems Engineering; Signal Processing; Computer Networks and Communications; Control and Optimization
Abstract :
[en] 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.
Disciplines :
Engineering, computing & technology: Multidisciplinary, general & others
Author, co-author :
Gray, Rebecca ; Department of Mechanical and Aerospace Engineering, Princeton University, Princeton, United States
Franci, Alessio ; Université de Liège - ULiège > Département d'électricité, électronique et informatique (Institut Montefiore) > Brain-Inspired Computing ; Department of Mathematics, Universidad Nacional Autónoma de México, Ciudad de México, Mexico
Srivastava, Vaibhav; Department of Electrical and Computer Engineering, Michigan State University, East Lansing, United States
Leonard, Naomi Ehrich ; Department of Mechanical and Aerospace Engineering, Princeton University, Princeton, United States
Language :
English
Title :
Multiagent Decision-Making Dynamics Inspired by Honeybees
Publication date :
June 2018
Journal title :
IEEE Transactions on Control of Network Systems
ISSN :
2325-5870
Publisher :
Institute of Electrical and Electronics Engineers Inc.
NSF - National Science Foundation ONR - Office of Naval Research UNAM - National Autonomous University of Mexico
Funding text :
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).
scite shows how a scientific paper has been cited by providing the context of the citation, a classification describing whether it supports, mentions, or contrasts the cited claim, and a label indicating in which section the citation was made.
Bibliography
T. D. Seeley and S. C. Buhrman, "Nest-site selection in honey bees: How well do swarms implement the "best-of-N" decision rule?" Behav. Ecol. Sociobiol., vol. 49, pp. 416-427, 2001.
I. D. Couzin et al., "Uninformed individuals promote democratic consensus in animal groups," Science, vol. 334, no. 6062, pp. 1578-1580, 2011.
C. Eikenaar, T. Klinner, L. Szostek, and F. Bairlein, "Migratory restlessness in captive individuals predicts actual departure in the wild," Biol. Lett., vol. 10, no. 4, 2014, Art. no. 20140154.
J. K. Parrish and L. Edelstein-Keshet, "Complexity, pattern, and evolutionary trade-offs in animal aggregation," Science, vol. 284, no. 5411, pp. 99-101, 1999.
J. Krause and G. D. Ruxton, Living in Groups. London, U.K.: Oxford Univ. Press, 2002.
D. J. T. Sumpter, Collective Animal Behavior. Princeton, NJ, USA: Princeton Univ. Press, 2010.
N. E. Leonard, "Multi-agent system dynamics:Bifurcation and behavior of animal groups," IFAC Annu. Reviews Control, vol. 38, no. 2, pp. 171-183, 2014.
M. Golubitsky and D. G. Schaeffer, "Singularities and Groups in Bifurcation Theory (Applied Mathematical Sciences 51). New York, NY, USA: Springer-Verlag, 1985.
A. Franci and R. Sepulchre, "Realization of nonlinear behaviors from organizing centers," in Proc. IEEE Conf. Decis. Control, Los Angeles, CA, USA, Dec. 2014, pp. 56-61.
F. Castaños and A. Franci, "Implementing robust neuromodulation in neuromorphic circuits," Neurocomputing, vol. 233, pp. 3-13, 2017.
J. J. Hopfield, "Neurons with graded response have collective computational properties like those of two-state neurons," Proc. Nat. Acad. Sci. USA, vol. 81, no. 10, pp. 3088-3092, 1984.
T. D. Seeley, P. K. Visscher, T. Schlegel, P. M. Hogan, N. R. Franks, and J. A. R. Marshall, "Stop signals provide cross inhibition in collective decision-making by honeybee swarms," Science, vol. 335, no. 6064, pp. 108-111, 2012.
D. Pais, P. M. Hogan, T. Schlegel, N. R. Franks, N. E. Leonard, and J. A. R. Marshall, "A mechanism for value-sensitive decision-making," PLoS One, vol. 8, no. 9, 2013, Art. no. e73216.
A. Reina, J. A. R. Marshall, V. Trianni, and T. Bose, "Model of the bestof-N nest-site selection process in honeybees," Phys. Rev. E, vol. 95, no. 5, 2017, Art. no. 052411.
A. Reina, G. Valentini, C. Fernández-Oto, M. Dorigo, and V. Trianni, "A design pattern for decentralised decision making," PLoS One, vol. 10, no. 10, 2015, Art. no. e0140950.
E. H. Abed and J.-H. Fu, "Local feedback stabilization and bifurcation control, Part I: Hopf bifurcations," Syst. Control Lett., vol. 7, pp. 11-17, 1986.
G. Chen, J. L. Moiola, and H. O. Wang, "Bifurcation control: Theories, methods, and applications," Int. J. Bifurcation Chaos, vol. 10, no. 3, pp. 511-548, 2000.
A. J. Krener, W. Kang, and D. E. Chang, "Control bifurcations," IEEE Trans. Autom. Control, vol. 49, no. 8, pp. 1231-1246, Aug. 2004.
J. J. Hopfield, "Neural networks and physical systems with emergent collective computational abilities," Proc. Nat. Acad. Sci. USA, vol. 79, no. 8, pp. 2554-2558, 1982.
J. Guckenheimer and P. Holmes, Nonlinear Oscillations, Dynamical Systems, and Bifurcations of Vector Fields (Applied Mathematical Sciences 42), 7th ed. New-York, NY, USA: Springer-Verlag, 2002.
A. Franci, V. Srivastava, and N. E. Leonard, "A realization theory for bio-inspired collective decision-making," arXiv:1503.08526.
A. Fontan and C.Altafini, "Multiequilibria analysis for a class of collective decision-making networked systems," IEEE Trans. Control Netw. Syst., to be published, doi: 10.1109/TCNS.2017.2774014.
A. Franci, V. Srivastava, R. Gray, and N. E. Leonard, "Symmetry breaking in unanimous collective decision-making between two near-equal value alternatives," in preparation.
M.W. Hirsch, "Stability and convergence in stronglymonotone dynamical systems," J. Reine Angew. Math., vol. 383, pp. 1-53, 1988.
W. Fulton and J. Harris, Representation Theory: A First Course, vol. 129. Berlin, Germany: Springer, 2013.
G. Strang, Linear Algebra and Its Applications. Pacific Grove, CA, USA: Brooks/Cole, 2006.
N. E. Leonard, T. Shen, B. Nabet, L. Scardovi, I. D. Couzin, and S. A. Levin, "Decision versus compromise for animal groups in motion," Proc. Nat. Acad. Sci., vol. 109, no. 1, pp. 227-232, 2012.
W. Govaerts and Y. A. Kuznetsov, MATCONT and CL MATCONT: Continuation toolboxes in MATLAB, Aug. 2015. [Online]. Available: https://sourceforge.net/projects/matcont/
D. Pais and N. E. Leonard, "Adaptive network dynamics and evolution of leadership in collective migration," Phys. D, vol. 267, pp. 81-93, 2014.
J. George, R. A. Freeman, and K. M. Lynch, "Robust dynamic average consensus algorithm for signals with bounded derivative," in Proc. Amer. Control Conf., 2017, pp. 352-357.
R. Aragues, G. Shi, D. V. Dimarogonas, C. Sagüés, K. H. Johansson, and Y. Mezouar, "Distributed algebraic connectivity estimation for undirected graphs with upper and lower bounds," Automatica, vol. 50, no. 12, pp. 3253-3259, 2014.
N. Fenichel, "Geometric singular perturbation theory for ordinary differential equations," J. Differ. Equ., vol. 31, no. 1, pp. 53-98, 1979.
S. Wiggins, Introduction to Applied Nonlinear Dynamical Systems and Chaos, vol. 2. Berlin, Germany: Springer, 2003.
M. Golubitsky and I. Stewart, The Symmetry Perspective: From Equilibrium to Chaos in Phase Space and Physical Space, vol. 200. Berlin, Germany: Springer, 2003.
M. Krupa and P. Szmolyan, "Extending geometric singular perturbation theory to nonhyperbolic points-fold and canard points in two dimensions," SIAM J. Math. Anal., vol. 33, no. 2, pp. 286-314, 2001.
N. Berglund and B. Gentz, Noise-Induced Phenomena in Slow-Fast Dynamical Systems. London, U.K.: Springer-Verlag, 2006.
C. K. R. Jones, "Geometric singular perturbation theory," in Dynamical Systems (Springer Lecture Notes in Mathematics 1609), R. Johnson, Ed. Berlin, Germany: Springer-Verlag, 1995, pp. 44-120.
This website uses cookies to improve user experience. Read more
Save & Close
Accept all
Decline all
Show detailsHide details
Cookie declaration
About cookies
Strictly necessary
Performance
Strictly necessary cookies allow core website functionality such as user login and account management. The website cannot be used properly without strictly necessary cookies.
This cookie is used by Cookie-Script.com service to remember visitor cookie consent preferences. It is necessary for Cookie-Script.com cookie banner to work properly.
Performance cookies are used to see how visitors use the website, eg. analytics cookies. Those cookies cannot be used to directly identify a certain visitor.
Used to store the attribution information, the referrer initially used to visit the website
Cookies are small text files that are placed on your computer by websites that you visit. Websites use cookies to help users navigate efficiently and perform certain functions. Cookies that are required for the website to operate properly are allowed to be set without your permission. All other cookies need to be approved before they can be set in the browser.
You can change your consent to cookie usage at any time on our Privacy Policy page.
