Tag Mechanisms Evaluated for Coordination in Open Multi-Agent Systems

  • Isaac Chao
  • Oscar Ardaiz
  • Ramon Sanguesa
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 4995)


Tags are arbitrary social labels carried by agents. When agents interact preferentially with those sharing the same Tag, groups are formed around similar Tags. This property can be used to achieve desired group coordination by evolving agent’s Tags through a group selection process. In this paper Tags performance is for the first time compared by simulation with alternative mechanisms for coordinated learning in multi-agent systems populations. We target open systems, hence we do not make costly assumptions on agent capabilities (rational or computational). It is a requirement that coordination strategies prove simple to implement and scalable. We build a simulator incorporating competition and cooperation scenarios modeled as one-shot repeated games between agents. Tags prove to be a very good coordination mechanism in both, cooperation building in competitive scenarios and agent behavior coordination in fully cooperative scenarios.


Tags group selection multi-agent systems coordination prisoner’s dilemma cooperative games 


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  1. 1.
    Hales, D. (2000) Cooperation without Space or Memory: Tags, Groups and the Prisoner’s Dilemma. In Moss, S., Davidsson, P. (Eds.) Multi-Agent-Based Google Scholar
  2. 2.
    Sycara, K.: Multiagent Systems. AI Magazine 10(2), 79–93 (1998)Google Scholar
  3. 3.
    J. Holland. The effects of labels (Tags) on social interactions. Working Paper Santa Fe Institute 93-10-064 (1993) Google Scholar
  4. 4.
    Golder, S.A., Huberman, B.A.: The Structure of Collaborative Tagging Systems.Information Dynamics Lab, HP Labs (Visited November 24, 2005)Google Scholar
  5. 5.
    Riolo, R.: The efects of Tag-mediated selection of partners in evolving populations playing the iterated prisoners dilemma. Nature 414, 441–443 (2000)CrossRefGoogle Scholar
  6. 6.
    McDonald, A., Sen, S.: The Success and Failure of Tag Mediated Evolution of Cooperation. In: Tuyls, K., ’t Hoen, P.J., Verbeeck, K., Sen, S. (eds.) LAMAS 2005. LNCS (LNAI), vol. 3898, pp. 155–164. Springer, Heidelberg (2006)CrossRefGoogle Scholar
  7. 7.
    Arteconi, S., Hales, D., Babaoglu, O.: Greedy Cheating Liars and the Fools Who Believe Them. In: Brueckner, S.A., Hassas, S., Jelasity, M., Yamins, D. (eds.) ESOA 2006. LNCS (LNAI), vol. 4335. Springer, Heidelberg (2007)CrossRefGoogle Scholar
  8. 8.
    Hales, D.: From Selfish Nodes to Cooperative Networks – Emergent Link-based Incentives in Peer-to-Peer Networks. In: Proceedings of The Fourth IEEE International Conference on Peer-to-Peer Computing (p2p2004), Zurich, Switzerland, August 25-27, 2004. IEEE Computer Society Press, Los Alamitos (2004)Google Scholar
  9. 9.
    Hales, D., Patarin, S.: Feature: Computational Sociology for Systems In the Wild: The Case of BitTorrent. IEEE Distributed Systems Online 6(7) (2005)Google Scholar
  10. 10.
    Hales, D., Babaoglu, O.: Towards Automatic Social Bootstrapping of Peer-to-Peer Protocols. ACM SIGOPS Operating Systems Review (Special Issue on Self-Organizing Systems) 40(3) (July 2006)Google Scholar
  11. 11.
    Weikum, G., Triantafillou, P., Hales, D., Schindelhauer, C.: Towards Self-Organizing Query Routing and Processing for Peer-to-Peer WebSearch. In: Proceedings of the European Conference on Complex Systems (ECCS 2005), Paris, France, November 14, 2005. i6doc, Belgium (2005) (in press)Google Scholar
  12. 12.
    Chao, I., Ardaiz, O., Sanguesa, R.: Tag Mechanisms Applied to Open Grid Virtual Organizations Management. In: Anthony, R., Butler, A., Ibrahim, M., Eymann, T., Veit, D.J. (eds.) Proceedings of the Joint Smart Grid Technologies (SGT) and Engineering Emergence for Autonomic Systems (EEAS) Workshop, Dublin, Ireland, pp. 22–29 (2006)Google Scholar
  13. 13.
    Mollona, E., Hales, D.: Modeling Firm Skill-Set Dynamics as a Complex System. In: Proceedings of the European Conference on Complex Systems (ECCS 2005), Paris, France, November 14, i6doc, Belgium (in press, 2005)Google Scholar
  14. 14.
    Next Generation Grids Expert Group Report 3, Future for European Grids: GRIDs and Service Oriented Knowledge UtilitiesGoogle Scholar
  15. 15.
    Howley, E., O’Riordan, C.: The Emergence of Cooperation among Agents using Simple Fixed Bias Tagging. In: IEEE Congress on Evolutionary Computation, September 2-5 (2005)Google Scholar
  16. 16.
    Hales, D.: The Evolution of Specialization in Groups. In: Lindemann, G., Moldt, D., Paolucci, M. (eds.) RASTA 2002. LNCS (LNAI), vol. 2934. Springer, Heidelberg (2004)Google Scholar
  17. 17.
    The Emergence of Symbiotic Groups Resulting from Skill-Differentiation and Tags, Bruce Edmonds. JASSS 9(1), January 31(2006)Google Scholar
  18. 18.
    Zohar, A., Rosenschein, J.S.: Using Tags to Evolve Trust and Cooperation Between Groups. In: The Fourth International Joint Conference on Autonomous Agents and Multiagent Systems, Utrecht, July 2005, The Netherlands, pp. 1199–1200 (2005)Google Scholar
  19. 19.
    Floortje Alkemade, D.D.B., van Bragt, J.A.: La Poutré: Stabilization of Tag-mediated interaction by sexual reproduction in an evolutionary agent system. Inf. Sci. 170(1), 101–119 (2005)CrossRefGoogle Scholar
  20. 20.
    Cohen, M., Riolo, R., Axelrod, R.: The emergence of social organization in the Prisoner’s Dilemma: how context-preservation and other factors promote cooperation Santa Fe Institute Working Paper 99-01-002 (1999)Google Scholar
  21. 21.
    Hamilton, W.D.: Man and Beast: Comparitive Social Behaviour. In: Eisenberg, J.F., Dillon, W.S. (eds.). Smithsonian Press, Washington (1971)Google Scholar
  22. 22.
    Coalition Formation: Towards Feasible Solutions. Fundamenta Informaticae 63(2-3), 107–124 (2004)Google Scholar
  23. 23.
    Lerman, K., Shehory, O.: Coalition formation for large-scale electronic markets. In: Proc. of ICMAS 2000, Boston, MA, pp. 167–174 (2000)Google Scholar
  24. 24.
    Kraus, S., Shehory, O., Taase, G.: Coalition formation with uncertain heterogeneous information. In: Proc. Of AAMAS 2003, Melbourne, Australia, pp. 1–8 (2003)Google Scholar
  25. 25.
    Axelrod, R.: The Evolution of Cooperation. Science 211(4489), 1390–1396 (1981)CrossRefMathSciNetGoogle Scholar
  26. 26.
    Nowak, M., Sigmund, K.: A strategy of winstay,lose-shift that outperforms tit-for-tat in the prisoner’sdilemma game. Nature 364, 56–58 (1993)CrossRefGoogle Scholar
  27. 27.
    Shoham, Y., Grenager, T., Powers, R.: Multi-agent reinforcement learning: A critical survey. Tech.rep., Stanford University (2003) Google Scholar
  28. 28.
    Wakano, J.Y., Yamamura, N.: A Simple Learning Strategy that Realizes Robust Cooperation Better than Pavlov in Iterated Prisoners’ Dilemma. J. Ethology 19, 9–15 (2001)CrossRefGoogle Scholar
  29. 29.
    Watkins, C.: Learning from Delayed Rewards, Thesis, University of Cambidge,England (1989)Google Scholar
  30. 30.
    de Cote, J.E.M., Lazaric, A., Restelli, M.: Learning to cooperate in multi-agent social dilemmas. In: AAMAS 2006, pp. 783–785 (2006)Google Scholar
  31. 31.
    Sandholm, T., Lesser, V.: Coalitions Among Computationally Bounded Agents. Artificial Intelligence, special issue on principles of multiagent systems 94(1) (1997)Google Scholar
  32. 32.
    Willensdorfer, M., Nowak, M.A.: Mutation in evolutionary games can increase average fitness at equilibrium. J. theor. Biol. 237, 355–362 (2005)CrossRefMathSciNetGoogle Scholar
  33. 33.
    Bowling, M.: Convergence problems of general-sum multia-gent reinforcement learning. In: Proceedings of the Seventeenth International, Conference on Machine Learning, pp. 89–94 (2000)Google Scholar
  34. 34.
    Durfee, E.H., Rosenschein, J.S.: Distributed Problem Solving and Multi-Agent Systems: Comparisons and Examples. In: Proc. 13th Int’l Distributed Artificial Intelligence Workshop, pp. 94–104 (1994)Google Scholar
  35. 35.
    Chao, I., Ardaiz, O., Sanguesa, R.: A Group Selection Pattern for agent-based Virtual Organizations coordination in Grids. In: International Conference on Grid computing, High-Performance and Distributed Applications (GADA 2007), Vilamoura, Algarve, Portugal, November 29- 30 (2007)Google Scholar
  36. 36.
    Chao, I., Ardaiz, O., Sanguesa, R.: A Group Selection Pattern Optimizing Job Scheduling in Decentralized Grid Markets. Poster accepted to the International Conference on Grid computing, High-Performance and Distributed Applications (GADA 2007), Vilamoura, Algarve, Portugal, November 29 - 30 (2007)Google Scholar
  37. 37.
    Nowak, M.A.: Five rules for the evolution of cooperation. Science 314, 1560–1563 (2006)CrossRefGoogle Scholar
  38. 38.
    Gotts, N.M., Polhill, J.G., Law, A.N.R.: Agent-based simulation in the study of social dilemmas. Artificial Intelligence Review 19, 3–92 (2003)CrossRefGoogle Scholar
  39. 39.
    Eymann, T., Reinicke, M., Streitberger, W., Rana, O., Joita, L., Neumann, D., Schnizler, B., Veit, D., Ardaiz, O., Chacin, P., Chao, I., FreiTag, F., Navarro, L., Catalano, M., Gallegati, M., Giulioni, G., Schiaffino, R.C., Zini, F.: Catallaxy-based Grid Markets. In: Veit, D.J., Eymann, T., Jennings, N.R., Müller, J.P. (eds.) Multiagent and Grid Systems Issue: Smart Grid Technologies & Market Models, vol. 1(4), pp. 297–307 (2005)Google Scholar
  40. 40.
    Galstyan, A., Czajkowski, K., Lerman, K.: Resource Allocation in the Grid with Learning Agents. Journal of Grid Computing 3(1–2), 91–100 (2005)CrossRefGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2008

Authors and Affiliations

  • Isaac Chao
    • 1
  • Oscar Ardaiz
    • 2
  • Ramon Sanguesa
    • 1
  1. 1.Informatics DepartmentPolytechnic University of CataloniaSpain
  2. 2.Informatics DepartmentPublic University of NavarraSpain

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