Population Coding: A New Design Paradigm for Embodied Distributed Systems

  • Heiko HamannEmail author
  • Gabriele Valentini
  • Marco DorigoEmail author
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 9882)


Designing embodied distributed systems, such as multi-robot systems, is challenging especially if the individual components have limited capabilities due to hardware restrictions. In self-organizing systems each component has only limited information and a global, organized system behavior (macro-level) has to emerge from local interactions only (micro-level). A general, structured design approach to self-organizing distributed systems is still lacking. We develop a general approach based on behaviorally heterogeneous systems. Inspired by the concept of population coding from neuroscience, we show in two case studies how designing an embodied distributed system is reduced to picking the right components from a predefined set of controller types. In this way, the design challenge is reduced to an optimization problem that can be solved by a variety of optimization techniques. Our approach is applicable to scenarios that allow for representing the component behavior as (probabilistic) finite state machine. We anticipate the paradigm of population coding to be applicable to a wide range of distributed systems.


Finite State Machine User Input Task Allocation Macroscopic Behavior Population Code 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.


  1. 1.
    Abelson, H., Allen, D., Coore, D., Hanson, C., Homsy, G., Knight, T., Nagpal, R., Rauch, E., Sussman, G., Weiss, R.: Amorphous computing. Commun. ACM 43(5), 74–82 (2000)CrossRefGoogle Scholar
  2. 2.
    Beal, J., Dulman, S., Usbeck, K., Viroli, M., Correll, N.: Organizing the aggregate: languages for spatial computing. In: Formal and Practical Aspects of Domain-Specific Languages, pp. 436–501. Information Science Reference (2012)Google Scholar
  3. 3.
    Beckers, R., Holland, O.E., Deneubourg, J.L.: From local actions to global tasks: stigmergy and collective robotics. Artif. Life 4, 181–189 (1994)Google Scholar
  4. 4.
    Berman, S., Halasz, A., Hsieh, M., Kumar, V.: Optimized stochastic policies for task allocation in swarms of robots. IEEE Trans. Robot. 25(4), 927–937 (2009)CrossRefGoogle Scholar
  5. 5.
    Brutschy, A., Pini, G., Pinciroli, C., Birattari, M., Dorigo, M.: Self-organized task allocation to sequentially interdependent tasks in swarm robotics. Auton. Agents Multi-Agent Syst. 28(1), 101–125 (2014)CrossRefGoogle Scholar
  6. 6.
    Dorigo, M., Birattari, M., Brambilla, M.: Swarm robotics. Scholarpedia 9(1), 1463 (2014)CrossRefGoogle Scholar
  7. 7.
    Dorigo, M., et al.: Swarmanoid: a novel concept for the study of heterogeneous robotic swarms. IEEE Robot. Autom. Mag. 20, 60–71 (2013)CrossRefGoogle Scholar
  8. 8.
    Dressler, F.: Self-organization in Sensor and Actor Networks. Wiley, New York (2008)Google Scholar
  9. 9.
    Ferrante, E., Turgut, A.E., Duez-Guzmn, E., Dorigo, M., Wenseleers, T.: Evolution of self-organized task specialization in robot swarms. PLoS Comput. Biol. 11(8), 1–21 (2015)CrossRefGoogle Scholar
  10. 10.
    Georgopoulos, A.P., Schwartz, A.B., Kettner, R.E.: Neuronal population coding of movement direction. Science 233(4771), 1416–1419 (1986)CrossRefGoogle Scholar
  11. 11.
    Gerkey, B.P., Matarić, M.J.: A formal analysis and taxonomy of task allocation in multi-robot systems. Int. J. Robot. Res. 23(9), 939–954 (2004)CrossRefGoogle Scholar
  12. 12.
    Hamann, H.: Towards swarm calculus: Urn models of collective decisions and universal properties of swarm performance. Swarm Intell. 7(2–3), 145–172 (2013)CrossRefGoogle Scholar
  13. 13.
    Hamann, H., Valentini, G., Khaluf, Y., Dorigo, M.: Derivation of a micro-macro link for collective decision-making systems. In: Bartz-Beielstein, T., Branke, J., Filipič, B., Smith, J. (eds.) PPSN 2014. LNCS, vol. 8672, pp. 181–190. Springer, Heidelberg (2014)Google Scholar
  14. 14.
    Hamann, H., Wörn, H.: A framework of space-time continuous models for algorithm design in swarm robotics. Swarm Intell. 2(2–4), 209–239 (2008)CrossRefGoogle Scholar
  15. 15.
    Hogg, T.: Coordinating microscopic robots in viscous fluids. Auton. Agents Multi-Agent Syst. 14(3), 271–305 (2006)MathSciNetCrossRefGoogle Scholar
  16. 16.
    Kengyel, D., Hamann, H., Zahadat, P., Radspieler, G., Wotawa, F., Schmickl, T.: Potential of heterogeneity in collective behaviors: a case study on heterogeneous swarms. In: Chen, Q., Torroni, P., Villata, S., Hsu, J. (eds.) PRIMA 2015. LNCS, vol. 9387, pp. 201–217. Springer, Heidelberg (2015)Google Scholar
  17. 17.
    Lenaghan, S., Wang, Y., Xi, N., Fukuda, T., Tarn, T., Hamel, W., Zhang, M.: Grand challenges in bioengineered nanorobotics for cancer therapy. IEEE Trans. Biomed. Eng. 60(3), 667–673 (2013)CrossRefGoogle Scholar
  18. 18.
    Pouget, A., Dayan, P., Zemel, R.: Information processing with population codes. Nat. Rev. Neurosci. 1, 125–132 (2000)CrossRefGoogle Scholar
  19. 19.
    Prorok, A., Hsieh, M.A., Kumar, V.: Fast redistribution of a swarm of heterogeneous robots. In: International Conference on Bio-inspired Information and Communications Technologies (BICT) (2015)Google Scholar
  20. 20.
    Valentini, G., Ferrante, E., Hamann, H., Dorigo, M.: Collective decision with 100 Kilobots: speed versus accuracy in binary discrimination problems. Auton. Agents Multi-Agent Syst. 30(3), 553–580 (2015)CrossRefGoogle Scholar
  21. 21.
    Valentini, G., Hamann, H., Dorigo, M.: Global-to-local design for self-organized task allocation in swarms. Technical report TR/IRIDIA/2016-002, IRIDIA, Université Libre de Bruxelles, Brussels, Belgium, March 2016Google Scholar

Copyright information

© Springer International Publishing Switzerland 2016

Authors and Affiliations

  1. 1.Department of Computer Science, Heinz Nixdorf InstituteUniversity of PaderbornPaderbornGermany
  2. 2.IRIDIAUniversité Libre de BruxellesBrusselsBelgium

Personalised recommendations