Swarm Intelligence

, Volume 5, Issue 3–4, pp 305–327 | Cite as

Majority-rule opinion dynamics with differential latency: a mechanism for self-organized collective decision-making

  • Marco A. Montes de Oca
  • Eliseo Ferrante
  • Alexander Scheidler
  • Carlo Pinciroli
  • Mauro Birattari
  • Marco Dorigo
Article

Abstract

Collective decision-making is a process whereby the members of a group decide on a course of action by consensus. In this paper, we propose a collective decision-making mechanism for robot swarms deployed in scenarios in which robots can choose between two actions that have the same effects but that have different execution times. The proposed mechanism allows a swarm composed of robots with no explicit knowledge about the difference in execution times between the two actions to choose the one with the shorter execution time. We use an opinion formation model that captures important elements of the scenarios in which the proposed mechanism can be used in order to predict the system’s behavior. The model predicts that when the two actions have different average execution times, the swarm chooses with high probability the action with the shorter average execution time. We validate the model’s predictions through a swarm robotics experiment in which robot teams must choose one of two paths of different length that connect two locations. Thanks to the proposed mechanism, a swarm made of robot teams that do not measure time or distance is able to choose the shorter path.

Keywords

Opinion dynamics Differential latency Collective decision-making Self-organization Swarm intelligence Swarm robotics 

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

Supplementary material

(AVI 16.3 MB)

References

  1. Bonani, M., Longchamp, V., Magnenat, S., Rétornaz, P., Burnier, D., Roulet, G., Vaussard, F., Bleuler, H., & Mondada, F. (2010). The MarXbot, a miniature mobile robot opening new perspectives for the collective-robotic research. In Proceedings of the IEEE/RSJ international conference on intelligent robots and systems (IROS 2010) (pp. 4187–4193). Piscataway: IEEE Press. Google Scholar
  2. Camazine, S., Deneubourg, J.-L., Franks, N. R., Sneyd, J., Theraulaz, G., & Bonabeau, E. (2001). Self-organization in biological systems. Princeton: Princeton University Press. Google Scholar
  3. Campo, A., Garnier, S., Dédriche, O., Zekkri, M., & Dorigo, M. (2010a). Self-organized discrimination of resources. PLoS ONE, 6(5), e19888. CrossRefGoogle Scholar
  4. Campo, A., Gutiérrez, Á., Nouyan, S., Pinciroli, C., Longchamp, V., Garnier, S., & Dorigo, M. (2010b). Artificial pheromone for path selection by a foraging swarm of robots. Biological Cybernetics, 103(5), 339–352. CrossRefGoogle Scholar
  5. Castellano, C., Fortunato, S., & Loreto, V. (2009). Statistical physics of social dynamics. Reviews of Modern Physics, 81(2), 591–646. CrossRefGoogle Scholar
  6. Chakrabarti, B., Chakraborti, A., & Chatterjee, A. (Eds.) (2006). Econophysics and sociophysics: trends and perspectives. Weinheim: Wiley-VCH. Google Scholar
  7. Couzin, I. D., & Krause, J. (2003). Self-organization and collective behavior in vertebrates. Advances in the Study of Behavior, 32, 1–75. CrossRefGoogle Scholar
  8. Ducatelle, F., Di Caro, G., & Gambardella, L. M. (2010). Cooperative self-organization in a heterogeneous swarm robotic system. In Proceedings of the genetic and evolutionary computation conference (GECCO 2010) (pp. 87–94). New York: ACM. Google Scholar
  9. Ferrante, E., Brambilla, M., Birattari, M., & Dorigo, M. Socially-mediated negotiation for obstacle avoidance in collective transport. In Proceedings of the 10th international symposium on distributed autonomous robotic systems (DARS 2010). Berlin: Springer (2011, to appear). Google Scholar
  10. Fujisawa, R., Dobata, S., Kubota, D., Imamura, H., & Matsuno, F. (2008a). Dependency by concentration of pheromone trail for multiple robots. In LNCS: Vol. 5217. Proceedings of the international conference on ant colony optimization and swarm intelligence (ANTS 2008) (pp. 283–290). Berlin: Springer. CrossRefGoogle Scholar
  11. Fujisawa, R., Imamura, H., Hashimoto, T., & Matsuno, F. (2008b). Communication using pheromone field for multiple robots. In Proceedings of the IEEE/RSJ international conference on intelligent robots and systems (IROS 2008) (pp. 1391–1396). Piscataway: IEEE Press. Google Scholar
  12. Galam, S. (1986). Majority rule, hierarchical structures, and democratic totalitarianism: A statistical approach. Journal of Mathematical Psychology, 30(4), 426–434. CrossRefMATHGoogle Scholar
  13. Garnier, S., Tache, F., Combe, M., Grimal, A., & Theraulaz, G. (2007). Alice in pheromone land: An experimental setup for the study of ant-like robots. In Proceedings of the IEEE swarm intelligence symposium (SIS 2007) (pp. 37–44). Piscataway: IEEE Press. CrossRefGoogle Scholar
  14. 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. Adaptive Behavior, 17(2), 109–133. CrossRefGoogle Scholar
  15. Goss, S., Aron, S., Deneubourg, J.-L., & Pasteels, J. M. (1989). Self-organized shortcuts in the argentine ant. Naturwissenschaften, 76(12), 579–581. CrossRefGoogle Scholar
  16. Granovetter, M. (1978). Threshold models of collective behavior. American Journal of Sociology, 83(6), 1420–1443. CrossRefGoogle Scholar
  17. Gutiérrez, Á., Campo, A., Monasterio-Huelin, F., Magdalena, L., & Dorigo, M. (2010). Collective decision-making based on social odometry. Neural Computing & Applications, 19(6), 807–823. CrossRefGoogle Scholar
  18. Hamman, H., Szymanski, M., & Worn, H. (2007). Orientation in a trail network by exploiting its geometry for swarm robotics. In Proceedings of the IEEE swarm intelligence symposium (SIS 2007) (pp. 310–315). Piscataway: IEEE Press. CrossRefGoogle Scholar
  19. Helbing, D. (2010). Quantitative sociodynamics. Stochastic methods and models of social interaction processes (2nd ed.) Berlin: Springer. MATHGoogle Scholar
  20. Herianto, H., & Kurabayashi, D. (2009). Realization of an artificial pheromone system in random data carriers using RFID tags for autonomous navigation. In IEEE international conference on robotics and automation (ICRA 2009) (pp. 2288–2293). Piscataway: IEEE Press. CrossRefGoogle Scholar
  21. Krapivsky, P. L., & Redner, S. (2003). Dynamics of majority rule in two-state interacting spin systems. Physical Review Letters, 90(23), 238701. CrossRefGoogle Scholar
  22. Kuwana, Y., Shimoyama, I., & Miura, H. (1995). Steering control of a mobile robot using insect antennae. In Proceedings of the IEEE/RSJ international conference on intelligent robots and systems (IROS 1995) (Vol. 2, pp. 530–535), Piscataway: IEEE Press. Google Scholar
  23. Lambiotte, R., Saramäki, J., & Blondel, V. D. (2009). Dynamics of latent voters. Physical Review. E, Statistical Physics, Plasmas, Fluids, and Related Interdisciplinary Topics, 79(4), 046107. CrossRefGoogle Scholar
  24. Mamei, M., & Zambonelli, F. (2005). Physical deployment of digital pheromones through RFID technology. In Proceedings of the international joint conference on autonomous agents and multiagent systems (AAMAS 2005) (pp. 1353–1354). New York: ACM. CrossRefGoogle Scholar
  25. Mayet, R., Roberz, J., Schmickl, T., & Crailsheim, K. (2010). Antbots: A feasible visual emulation of pheromone trails for swarm robots. In LNCS: Vol. 6234. Proceedings of the international conference on swarm intelligence (ANTS 2010) (pp. 84–94). Berlin: Springer. Google Scholar
  26. Montes de Oca, M. A., Ferrante, E., Scheidler, A., Pinciroli, C., Birattari, M., Dorigo, M. (2011). Majority-rule opinion dynamics with differential latency: Supplementary information webpage. http://iridia.ulb.ac.be/supp/IridiaSupp2010-014/.
  27. Nagasawa, S., Kanzaki, R., & Shimoyama, I. (1999). Study of a small mobile robot that uses living insect antennae as pheromone sensors. In Proceedings of the IEEE/RSJ international conference on intelligent robots and systems (IROS 1999) (pp. 555–560). Piscataway: IEEE Press. Google Scholar
  28. Nouyan, S., Campo, A., & Dorigo, M. (2008). Path formation in a robot swarm: self-organized strategies to find your way home. Swarm Intelligence, 2(1), 1–23. CrossRefGoogle Scholar
  29. Nouyan, S., Gross, R., Bonani, M., Mondada, F., & Dorigo, M. (2009). Teamwork in self-organized robot colonies. IEEE Transactions on Evolutionary Computation, 13(4), 695–711. CrossRefGoogle Scholar
  30. Nowak, M. A. (2006). Five rules for the evolution of cooperation. Science, 314(5805), 1560–1563. CrossRefGoogle Scholar
  31. Parker, C. A. C., & Zhang, H. (2009). Cooperative decision-making in decentralized multiple-robot systems: The best-of-N problem. IEEE/ASME Transactions on Mechatronics, 14(2), 240–251. CrossRefGoogle Scholar
  32. Parker, C. A. C., & Zhang, H. (2010). Collective unary decision-making by decentralized multiple-robot systems applied to the task-sequencing problem. Swarm Intelligence, 4(3), 199–220. CrossRefGoogle Scholar
  33. Payton, D., Daily, M., Estowski, R., Howard, M., & Lee, C. (2001). Pheromone robotics. Autonomous Robots, 11(3), 319–324. CrossRefMATHGoogle Scholar
  34. Pinciroli, C., Trianni, V., O’Grady, R., Pini, G., Brutschy, A., Brambilla, M., Mathews, N., Ferrante, E., Di Caro, G., Ducatelle, F., Stirling, T., Gutiérrez, Á., Gambardella, L. M., & Dorigo, M. (2011, in press). ARGoS: a modular, multi-engine simulator for heterogeneous swarm robotics. In Proceedings of the IEEE/RSJ international conference on intelligent robots and systems (IROS 2011). Piscataway: IEEE Press. Google Scholar
  35. Roberts, J., Stirling, T., Zufferey, J., & Floreano, D. (2009). 2.5D infrared range and bearing system for collective robotics. In Proceedings of the IEEE/RSJ international conference on intelligent robots and systems (IROS 2009) (pp. 3659–3664). Piscataway: IEEE Press. Google Scholar
  36. Russell, R. A. (1999). Ant trails—an example for robots to follow? In Proceedings of the IEEE international conference on robotics and automation (ICRA 1999) (pp. 2698–2703). Piscataway: IEEE Press. Google Scholar
  37. Scheidler, A. (2011). Dynamics of majority rule with differential latencies. Physical Review. E, Statistical Physics, Plasmas, Fluids, and Related Interdisciplinary Topics, 83(3), 031116. CrossRefGoogle Scholar
  38. Schelling, T. (1978). Micromotives and macrobehavior. New York: Norton. Google Scholar
  39. Schmickl, T., & Crailsheim, K. (2008). Trophallaxis within a robotic swarm: bio-inspired communication among robots in a swarm. Autonomous Robots, 25(1–2), 171–188. CrossRefGoogle Scholar
  40. Sugawara, K., Kazama, T., & Watanabe, T. (2004). Foraging behavior of interacting robots with virtual pheromone. In Proceedings of the IEEE/RSJ international conference on intelligent robots and systems (IROS 2004) (pp. 3074–3079). Piscataway: IEEE Press. Google Scholar
  41. Werger, B., & Matarić, M. (1996). Robotic “food” chains: Externalization of state and program for minimal-agent foraging. In Proceedings of the international conference on simulation of adaptive behavior: from animals to animats (SAB 1996) (pp. 625–634). Cambridge: MIT Press. Google Scholar
  42. Wessnitzer, J., & Melhuish, C. (2003). Collective decision-making and behaviour transitions in distributed ad hoc wireless networks of mobile robots: Target-hunting. In LNCS: Vol. 2801. Proceedings of the European conference on artificial life (ECAL 2003) (pp. 893–902). Berlin: Springer. Google Scholar

Copyright information

© Springer Science + Business Media, LLC 2011

Authors and Affiliations

  • Marco A. Montes de Oca
    • 1
    • 2
  • Eliseo Ferrante
    • 1
  • Alexander Scheidler
    • 1
  • Carlo Pinciroli
    • 1
  • Mauro Birattari
    • 1
  • Marco Dorigo
    • 1
  1. 1.IRIDIA, CoDEUniversité Libre de BruxellesBrusselsBelgium
  2. 2.Dept. of Mathematical SciencesUniversity of DelawareNewarkUSA

Personalised recommendations