Emergent Specialization in Swarm Systems

  • Ling Li
  • Alcherio Martinoli
  • Yaser S. Abu-Mostafa
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 2412)


Distributed learning is the learning process of multiple autonomous agents in a varying environment, where each agent has only partial information about the global task. In this paper, we investigate the influence of different reinforcement signals (local and global) and team diversity (homogeneous and heterogeneous agents) on the learned solutions. We compare the learned solutions with those obtained by systematic search in a simple case study in which pairs of agents have to collaborate in order to solve the task without any explicit communication. The results show that policies which allow teammates to specialize find an adequate diversity of the team and, in general, achieve similar or better performances than policies which force homogeneity. However, in this specific case study, the achieved team performances appear to be independent of the locality or globality of the reinforcement signal.


Team Performance Swarm Intelligence Reinforcement Signal Real Robot Successful Collaboration 
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.


Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.


  1. 1.
    Bonabeau, E., Dorigo, M., Théraulaz, G.: Swarm Intelligence: From Natural to Artificial Systems. Oxford University Press, New York (1999)zbMATHGoogle Scholar
  2. 2.
    Parrish, J.K., Hamner, W.M., eds.: Animal Groups in Three Dimensions. Cambridge University Press, New York (1997)Google Scholar
  3. 3.
    Martinoli, A., Mondada, F.: Collective and cooperative group behaviours: Biologically inspired experiments in robotics. In Khatib, O., Salisbury, J.K., eds.: Proceedings of the Fourth International Symposium on Experimental Robotics (1995). Lecture Notes in Control and Information Sciences, Vol. 223. Springer-Verlag, Berlin (1997) 3–10Google Scholar
  4. 4.
    Ijspeert, A.J., Martinoli, A., Billard, A., Gambardella, L.M.: Collaboration through the exploitation of local interactions in autonomous collective robotics: The stick pulling experiment. Autonomous Robots 11 (2001) 149–171zbMATHCrossRefGoogle Scholar
  5. 5.
    Lerman, K., Galstyan, A., Martinoli, A., Ijspeert, A.J.: A macroscopic analytical model of collaboration in distributed robotic systems. Artificial Life 7 (2001) 375–393CrossRefGoogle Scholar
  6. 6.
    Versino, C., Gambardella, L.M.: Learning real team solutions. In Weiß, G., ed.: Distributed Artificial Intelligence Meets Machine Learning: Learning in Multi-Agent Environments. Lecture Notes in Artificial Intelligence, Vol. 1221. Springer-Verlag, Berlin (1997) 40–61Google Scholar
  7. 7.
    Murciano, A., del R. Millán, J., Zamora, J.: Specialization in multi-agent systems through learning. Biological Cybernetics 76 (1997) 375–382zbMATHCrossRefGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2002

Authors and Affiliations

  • Ling Li
    • 1
  • Alcherio Martinoli
    • 1
  • Yaser S. Abu-Mostafa
    • 1
  1. 1.California Institute of TechnologyPasadenaUSA

Personalised recommendations