From Solitary to Collective Behaviours: Decision Making and Cooperation

  • Vito Trianni
  • Christos Ampatzis
  • Anders Lyhne Christensen
  • Elio Tuci
  • Marco Dorigo
  • Stefano Nolfi
Part of the Lecture Notes in Computer Science book series (LNCS, volume 4648)

Abstract

In a social scenario, establishing whether a collaboration is required to achieve a certain goal is a complex problem that requires decision making capabilities and coordination among the members of the group. Depending on the environmental contingencies, solitary actions may result more efficient than collective ones and vice versa. In robotics, it may be difficult to estimate the utility of engaging in collaboration versus remaining solitary, especially if the robots have only limited knowledge about the environment. In this paper, we use artificial evolution to synthesise neural controllers that let a homogeneous group of robots decide when to switch from solitary to collective actions based on the information gathered through time. However, being in a social scenario, the decision taken by a robot can influence—and is influenced itself—by the status of the other robots that are taking their own decisions at the same time. We show that the simultaneous presence of robots trying to decide whether to engage in a collective action or not can lead to cooperation in the decision making process itself.

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References

  1. 1.
    Parker, C.A.C., Zhang, H.: Biologically inspired decision making for collective robotic systems. In: Proc. of the 2004 IEEE/RSJ Int. Conf. on Intelligent Robots and Systems, pp. 375–380. IEEE Computer Society Press, Los Alamitos (2004)Google Scholar
  2. 2.
    Parker, C.A.C., Zhang, H.: Active versus passive expression of preference in the control of multiple-robot decision-making. In: Proc. of the 2005 IEEE/RSJ Int. Conf. on Intelligent Robots and Systems, pp. 3706–3711. IEEE Computer Society Press, Los Alamitos (2005)Google Scholar
  3. 3.
    Garnier, S., Jost, C., Jeanson, R., Gautrais, J., Asadpour, M., Caprari, G., Theraulaz, G.: Aggregation behaviour as a source of collective decision in a group of cockroach-like robots. In: Capcarrère, M.S., Freitas, A.A., Bentley, P.J., Johnson, C.G., Timmis, J. (eds.) ECAL 2005. LNCS (LNAI), vol. 3630, pp. 169–178. Springer, Heidelberg (2005)CrossRefGoogle Scholar
  4. 4.
    Kok, J.R., Spaan, M.T.J., Vlassis, N.: Multi-robot decision making using coordination graphs. In: Proc. of the 11th Int. Conf. on Advanced Robotics, pp. 953–957. IEEE Computer Society Press, Piscataway (2003)Google Scholar
  5. 5.
    Vlassis, N., Elhorst, R., Kok, J.R.: Anytime algorithms for multiagent decision making using coordination graphs. In: Proc. of the 2004 IEEE Conf. on System, Man and Cybernetics, pp. 953–957. IEEE Computer Society Press, Piscataway (2004)Google Scholar
  6. 6.
    Mondada, F., Pettinaro, G.C., Guignard, A., Kwee, I.V., Floreano, D., Deneubourg, J.-L., Nolfi, S., Gambardella, L.M., Dorigo, M.: SWARM-BOT: A new distributed robotic concept. Auton. Robots 17(2–3), 193–221 (2004)CrossRefGoogle Scholar
  7. 7.
    O’Grady, R., Groß, R., Bonani, M., Mondada, F., Dorigo, M.: Self-assembly on demand in a group of physical autonomous mobile robots navigating rough terrain. In: Capcarrère, M.S., Freitas, A.A., Bentley, P.J., Johnson, C.G., Timmis, J. (eds.) ECAL 2005. LNCS (LNAI), vol. 3630, pp. 272–281. Springer, Heidelberg (2005)CrossRefGoogle Scholar
  8. 8.
    Groß, R., Tuci, E., Bonani, M., Mondada, F., Dorigo, M.: Object transport by modular robots that self-assemble. In: Proc. of the 2006 IEEE Int. Conf. on Robotics and Automation, pp. 2558–2564. IEEE Computer Society Press, Los Alamitos (2006)CrossRefGoogle Scholar
  9. 9.
    Nouyan, S., Dorigo, M.: Chain based path formation in swarms of robots. In: Dorigo, M., Gambardella, L.M., Birattari, M., Martinoli, A., Poli, R., Stützle, T. (eds.) ANTS 2006. LNCS, vol. 4150, pp. 120–131. Springer, Heidelberg (2006)CrossRefGoogle Scholar
  10. 10.
    Trianni, V., Tuci, E., Dorigo, M.: Evolving functional self-assembling in a swarm of autonomous robots. In: Schaal, S., et al. (eds.) From Animals to Animats 8. Proc. of the 8th Int. Conf. on Simulation of Adaptive Behavior, pp. 405–414. MIT Press, Cambridge (2004)Google Scholar
  11. 11.
    Tuci, E., Trianni, V., Dorigo, M.: ’Feeling’ the flow of time through sensorimotor co-ordination. Conn. Sciene 16(4), 301–324 (2004)CrossRefGoogle Scholar
  12. 12.
    Ampatzis, C., Tuci, E., Trianni, V., Dorigo, M.: Evolution of signalling in a swarm of robots controlled by dynamic neural networks. In: Şahin, E., et al. (eds.) Swarm Robotics. LNCS, vol. 4433, pp. 173–188. Springer, Heidelberg (2007)CrossRefGoogle Scholar
  13. 13.
    Beer, R.D.: A dynamical systems perspective on agent-environment interaction. Art. Intell. 72, 173–215 (1995)CrossRefGoogle Scholar
  14. 14.
    Goldberg, D.E.: Genetic Algorithms in Search, Optimization and Machine Learning. Addison-Wesley, Reading (1989)MATHGoogle Scholar
  15. 15.
    Trianni, V., Ampatzis, C., Christensen, A.L., Tuci, E., Dorigo, M., Nolfi, S.: From solitary to collective behaviours: Decision making and cooperation – supplementary material. http://laral.istc.cnr.it/esm/trianni-etal-ecal2007.html

Copyright information

© Springer-Verlag Berlin Heidelberg 2007

Authors and Affiliations

  • Vito Trianni
    • 1
  • Christos Ampatzis
    • 2
  • Anders Lyhne Christensen
    • 2
  • Elio Tuci
    • 2
  • Marco Dorigo
    • 2
  • Stefano Nolfi
    • 1
  1. 1.LARAL, ISTC, CNRItaly
  2. 2.IRIDIA, CoDE, ULBBelgium

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