Skip to main content
SpringerLink
Log in

Analysis of emergent symmetry breaking in collective decision making

  • Swam Intelligence
  • Published:
Neural Computing and Applications Aims and scope Submit manuscript

Abstract

We investigate a simulated multi-agent system (MAS) that collectively decides to aggregate at an area of high utility. The agents’ control algorithm is based on random agent–agent encounters and is inspired by the aggregation behavior of honeybees. In this article, we define symmetry breaking, several symmetry breaking measures, and report the phenomenon of emergent symmetry breaking within our observed system. The ability of the MAS to successfully break the symmetry depends significantly on a local-neighborhood-based threshold of the agents’ control algorithm that determines at which number of neighbors the agents stop. This dependency is analyzed and two macroscopic features are determined that significantly influence the symmetry breaking behavior. In addition, we investigate the connection between the ability of the MAS to break symmetries and the ability to stay flexible in a dynamic environment.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6
Fig. 7
Fig. 8
Fig. 9
Fig. 10
Fig. 11

Similar content being viewed by others

References

  1. Yang CN (1952) The spontaneous magnetization of a two-dimensional Ising model. Phys Rev 85(5):808–816

    Article  MATH  Google Scholar 

  2. Anderson PW (1972) More is different. Science 177(4047):393–396

    Article  Google Scholar 

  3. Collier J (1996) Information originates in symmetry breaking. Symmetry Sci Cult 7:247–256

    MATH  Google Scholar 

  4. Tabony J, Job D (1992) Gravitational symmetry breaking in microtubular dissipative structures. PNAS 89(15):6948–6952

    Article  Google Scholar 

  5. Helbing D, Molnár P, Farkas IJ, Bolay K (2001) Self-organizing pedestrian movement. Environ Plann B Plann Des 28(3):361–383

    Article  Google Scholar 

  6. Deneubourg JL, Gregoire JC, Fort EL (1990) Kinetics of larval gregarious behavior in the bark beetle Dendroctonus micans (coleoptera: Scolytidae). J Insect Behav 3(2):169–182

    Article  Google Scholar 

  7. Deneubourg JL, Lioni A, Detrain C (2002) Dynamics of aggregation and emergence of cooperation. Biological Bulletin 202 (June 2002), pp 262–267

    Google Scholar 

  8. Theraulaz G, Bonabeau E, Nicolis SC, Solé RV, Fourcassié V, Blanco S, Fournier R, Joly JL, Fernández P, Grimal A, Dalle P, Deneubourg JL (2002) Spatial patterns in ant colonies. Proc Natl Acad Sci U S A 99(15):9645–9649

    Google Scholar 

  9. Ame JM, Rivault C, Deneubourg JL (2004) Cockroach aggregation based on strain odour recognition. Animal Behav 68:793–801

    Article  Google Scholar 

  10. Leoncini I, Rivault C (2005) Could species segregation be a consequence of aggregation processes? example of Periplaneta americana (l.) and P. fuliginosa (serville). Ethology 111(5):527–540

    Article  Google Scholar 

  11. Jeanson R, Rivault C, Deneubourg JL, Blanco S, Fournier R, Jost C, Theraulaz G (2005) Self-organized aggregation in cockroaches. Animal Behav 69:169–180

    Article  Google Scholar 

  12. Halloy J, Sempo G, Caprari G, Rivault C, Asadpour M, Tâche F, Saïd I, Durier V, Canonge S, Amé JM, Detrain C, Correll N, Martinoli A, Mondada F, Siegwart R, Deneubourg JL (2007) Social integration of robots into groups of cockroaches to control self-organized choices. Science 318(5853):1155–1158

    Article  Google Scholar 

  13. Sumpter DJT, Broomhead DS (2004) Shape and dynamics of thermoregulating honey bee clusters. J Theor Biol 204:1–14

    Article  Google Scholar 

  14. Schmickl T, Hamann H (2010) BEECLUST: a swarm algorithm derived from honeybees. In: Xiao Y, Hu F (eds) Bio-inspired computing and communication networks. Routledge

  15. Tyutyunov Y, Senina I, Arditi R (2004) Clustering due to acceleration in the response to population gradient: a simple self-organization model. Am Nat 164(6)

  16. Kernbach S, Thenius R, Kornienko O, Schmickl T (2009) Re-embodiment of honeybee aggregation behavior in an artificial micro-robotic swarm. Adapt Behav 17:237–259

    Article  Google Scholar 

  17. Seeley TD, Visscher PK (1991) Choosing a home: how the scouts in a honey bee swarm perceive the completion of their group decision making. Behav Ecol Sociobiol 54:511–520

    Article  Google Scholar 

  18. Franks NR, Mallon EB, Bray HE, Hamilton MJ, Mischler TC (2003) Strategies for choosing between alternatives with different attributes: exemplified by house-hunting ants. Animal Behav 65:215–223

    Article  Google Scholar 

  19. Franks NR, Pratt SC, Mallon EB, Britton NF, Sumpter DJT (2002) Information flow, opinion polling and collective intelligence in house-hunting social insects. Philos Trans R Soc Lond B Biol Sci 357:1567–1583

    Article  Google Scholar 

  20. Jeanson R, Deneubourg JL, Grimal A, Theraulaz G (2004) Modulation of individual behavior and collective decision-making during aggregation site selection by the ant messor barbarus. Behav Ecol Sociobiol 55:388–394

    Article  Google Scholar 

  21. Portha S, Deneubourg JL, Detrain C (2002) Self-organized asymmetries in ant foraging: a functional response to food type and colony needs. Behav Ecol 13(6):776–781

    Article  Google Scholar 

  22. Dussutour A, Fourcassié V, Helbing D, Deneubourg JL (2006) Optimal traffic organization in ants under crowded condition. Nature 428:70–73

    Article  Google Scholar 

  23. Nicolis SC, Deneubourg JL (1999) Emerging patterns and food recruitment in ants: an analytical study. J Theor Biol 198(4):575–592

    Article  Google Scholar 

  24. Saffre F, Furey R, Krafft B, Deneubourg JL (1999) Collective decision-making in social spiders: Dragline-mediated amplification process acts as a recruitment mechanism. J Theor Biol 198:507–517

    Article  Google Scholar 

  25. de Vries H, Biesmeijer JC (2002) Self-organization in collective honeybee foraging: emergence of symmetry breaking, cross inhibition and equal harvest-rate distribution. Behav Ecol Sociobiol 51(6):557–569

    Article  Google Scholar 

  26. Meyer B, Beekman M, Dussutour A (2008) Noise-induced adaptive decision-making in ant-foraging. In: Simulation of adaptive behavior (SAB), Number 5040 in LNCS, Springer, pp 415–425

  27. Nicolis SC, Dussutour A (2008) Self-organization, collective decision making and source exploitation strategies in social insects. Eur Phys J B 65:379–385

    Article  Google Scholar 

  28. Sharkey AJC (2007) Swarm robotics and minimalism. Connect Sci 19(3):245–260

    Article  Google Scholar 

  29. Schmickl T, Crailsheim K (2008) Trophallaxis within a robotic swarm: bio-inspired communication among robots in a swarm. Auton Robots 25(1–2):171–188

    Article  Google Scholar 

  30. Hamann H, Wörn H, Crailsheim K, Schmickl T (2008) Spatial macroscopic models of a bio-inspired robotic swarm algorithm. In: IEEE/RSJ 2008 international conference on intelligent robots and systems (IROS’08), Los Alamitos, CA, IEEE Press (2008), pp 1415–1420

  31. Garnier S, Gautrais J, Asadpour M, Jost C, Theraulaz G (2009) Self-organized aggregation triggers collective decision making in a group of cockroach-like robots. Adapt Behav 17(2):109–133

    Article  Google Scholar 

  32. Garnier S, Jost C, Jeanson R, Gautrais J, Asadpour M, Caprari G, Theraulaz G (2005) Aggregation behaviour as a source of collective decision in a group of cockroach-like-robots. In: Capcarrere M (ed) Advances in artificial life: 8th European conference, ECAL 2005, vol 3630 of LNAI, Springer, pp 169–178

  33. Camazine S, Deneuenbourg JL, Franks NR, Sneyd J, Theraulaz G, Bonabeau E (2001) Self-organization in biological systems (Princeton Studies in Complexity). University Presses of CA

  34. Schmickl T, Hamann H, Wörn H, Crailsheim K (2009) Two different approaches to a macroscopic model of a bio-inspired robotic swarm. Rob Auton Syst 57(9):913–921

    Article  Google Scholar 

  35. Bodi M, Thenius R, Schmickl T, Crailsheim K (2009) Robustness of two interacting robot swarms using the BEECLUST algorithm. In: MATHMOD 2009—6th Vienna international conference on mathematical modelling

  36. Schmickl T, Thenius R, Möslinger C, Radspieler G, Kernbach S, Crailsheim K (2008) Get in touch: Cooperative decision making based on robot-to-robot collisions. Auton Agent Multi Agent Syst 18(1):133–155

    Article  Google Scholar 

  37. Garnier S, Jost C, Gautrais J, Asadpour M, Caprari G, Jeanson R, Grimal A, Theraulaz G (2008) The embodiment of cockroach aggregation behavior in a group of micro-robots. Artif Life 14(4):387–408, PMID: 18573067

    Article  Google Scholar 

  38. Franks NR, Dornhaus A, Fitzsimmons JP, Stevens M (2003) Speed versus accuracy in collective decision making. Proc R Soc Lond B 270:2457–2463

    Article  Google Scholar 

  39. Seeley TD, Camazine S, Sneyd J (1991) Collective decision-making in honey bees: how colonies choose among nectar sources. Behav Ecol Sociobiol 28(4):277–290

    Article  Google Scholar 

Download references

Acknowledgments

The authors thank the anonymous reviewers for their helpful comments as well as Sibylle Hahshold, Martina Szopek, Gerald Radspieler and Ronald Thenius for providing us with data of honeybee experiments and for building the honeybee temperature arena. This work is supported by: EU-IST FET project I-SWARM, no. 507006; EU-IST-FET project ‘SYMBRION’, no. 216342; EU-ICT project ‘REPLICATOR’, no. 216240. Austrian Science Fund (FWF) research grants: P15961-B06 and P19478-B16. German Research Foundation (DFG) within the Research Training Group GRK 1194 Self-organizing Sensor-Actuator Networks.

Author information

Authors and Affiliations

  1. Artificial Life Lab of the Department of Zoology, Karl-Franzens University Graz, Universitätsplatz 2, 8010, Graz, Austria

    Heiko Hamann, Thomas Schmickl & Karl Crailsheim

  2. Universität Karslruhe (TH), Institute for Process Control and Robotics, Engler-Bunte-Ring 8, 76131, Karlsruhe, Germany

    Heinz Wörn

Authors
  1. Heiko Hamann
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Thomas Schmickl
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Heinz Wörn
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Karl Crailsheim
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Heiko Hamann.

Rights and permissions

Reprints and permissions

About this article

Cite this article

Hamann, H., Schmickl, T., Wörn, H. et al. Analysis of emergent symmetry breaking in collective decision making. Neural Comput & Applic 21, 207–218 (2012). https://doi.org/10.1007/s00521-010-0368-6

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s00521-010-0368-6

Keywords

Search

Navigation