Can Tags Build Working Systems? From MABS to ESOA

  • David Hales
  • Bruce Edmonds
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 2977)


We outline new results coming from multi-agent based social simulation (MABS) that harness ”tag-based” mechanisms in the production of self-organized behavior in simulation models. These tag mechanisms have, hitherto, been given sociological or biological interpretations. Here we attempt to move towards the application of these mechanisms to issues in self-organizing software engineering – i.e. how to program software agents that can self-organize to solve real problems. We also speculate how tags might inform the design of an unblockable P2P adaptive protocol layer.


Operating System Simulation Model Artificial Intelligence Communication Network Software Engineering 
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.


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  1. 1.
    Edmonds, B.: Learning and Exploiting Context in Agents. In: Proceedings of the 1st International Joint Conference on Autonomous Agents and Multiagent Systems (AAMAS), Bologna, Italy, July 2002, pp. 1231–1238. ACM Press, New York (2002)CrossRefGoogle Scholar
  2. 2.
    Epstein, J., Axtell, R.: Growing Artificial Societies: Social Science from the Bottom Up. The MIT Press, Cambridge (1996)Google Scholar
  3. 3.
    Gilbert, N., Doran, J. (eds.): Simulating Societies: the Computer Simulation of Social Phenomena. UCL Press, London (1994)Google Scholar
  4. 4.
    Gilbert, N., Conte, R. (eds.): Artificial Societies: the Computer Simulation of Social Life. UCL Press, London (1995)Google Scholar
  5. 5.
    Hales, D.: Cooperation without Space or Memory - Tags, Groups and the Prisoner’s Dilemma. In: Moss, S., Davidsson, P. (eds.) MABS 2000. LNCS (LNAI), vol. 1979, pp. 157–166. Springer, Heidelberg (2001)CrossRefGoogle Scholar
  6. 6.
    Hales, D.: Tag Based Co-operation in Artificial Societies. Ph.D. Thesis, Department of Computer Science, University of Essex, UK (2001)Google Scholar
  7. 7.
    Hales, D.: Evolving Specialisation, Altruism and Group-Level Optimisation Using Tags. In: Sichman, J.S., Bousquet, F., Davidsson, P. (eds.) MABS 2002. LNCS (LNAI), vol. 2581, pp. 26–35. Springer, Heidelberg (2003)CrossRefGoogle Scholar
  8. 8.
    Hales, D., Edmonds, B.: Evolving Social Rationality for MAS using “Tags”. In: Proceedings ofthe AAMAS 2003 Conference, Melbourne, ACM Press, New York (2003)Google Scholar
  9. 9.
    Hardin, G.: The tragedy ofthe commons. Science 162, 1243–1248 (1968)CrossRefGoogle Scholar
  10. 10.
    Hogg, L.M., Jennings, N.R.: Socially Rational Agents. In: Proc. AAAI Fall symposium on Socially Intelligent Agents, Boston, Mass., November 8-10, pp. 61–63 (1997)Google Scholar
  11. 11.
    Holland, J.: The Effect of Labels (Tags) on Social Interactions. SFI Working Paper 9310-064. Santa Fe Institute, Santa Fe, NM (1993)Google Scholar
  12. 12.
    Jennings, N.R.: Agent-based Computing: Promise and Perils. In: Proc. 16th Int. Joint Conf. on Artificial Intelligence (IJCAI 1999), Stockholm, Sweden, pp. 1429–1436 (1999) (Computers and Thought award invited paper)Google Scholar
  13. 13.
    Jennings, N.R.: On Agent-Based Software Engineering. Artificial Intelligence 117(2), 277–296 (2000)zbMATHCrossRefGoogle Scholar
  14. 14.
    Jennings, N., Campos, J.: Towards a Social Level Characterization of Socially Responsible Agents. IEE Proceedings on Software Engineering 144(1), 11–25 (1997)CrossRefGoogle Scholar
  15. 15.
    Kalenka, S.: Modelling Social Interaction Attitudes in Multi-Agent Systems. Ph.D Thesis. Department of Electronics and Computer Science, Southampton University (2001)Google Scholar
  16. 16.
    Kalenka, S., Jennings, N.R.: Socially Responsible Decision Making by Autonomous Agents. In: Korta, K., Sosa, E., Arrazola, X. (eds.) Cognition, Agency and Rationality, pp. 135–149. Kluwer, Dordrecht (1999)Google Scholar
  17. 17.
    Riolo, R.: The Effects of Tag-Mediated Selection of Partners in Evolving Populations Playing the Iterated Prisoner’s Dilemma. SFI Working Paper 73-02-016. Santa Fe Institute, Santa Fe, NM (1997)Google Scholar
  18. 18.
    Riolo, R., Cohen, M.D., Axelrod, R.: Cooperation without Reciprocity. Nature 414, 441–443 (2001)CrossRefGoogle Scholar
  19. 19.
    Russell, S.: Rationality and Intelligence. Artificial Intelligence 94(1), 55–57 (1997)CrossRefGoogle Scholar
  20. 20.
    Russell, S., Norvig, P.: Artificial Intelligence: A Modern Approach. Prentice-Hall, Englewood Cliffs (1995)zbMATHGoogle Scholar
  21. 21.
    Russell, S., Wefald, E.: Do The Right Thing: Studies in Rationality. MIT Press, Cambridge (1991)Google Scholar
  22. 22.
    Sigmund, K., Nowak, M.A.: Tides of tolerance. Nature 414, 403–405 (2001)CrossRefGoogle Scholar
  23. 23.
    Moss, S., Gaylard, H., Wallis, S., Edmonds, B.: SDML: A Multi-Agent Language for Organizational Modelling. Computational and Mathematical Organization Theory 4, 43–69 (1998)CrossRefGoogle Scholar
  24. 24.
    Moss, S.: Policy analysis from first principles. Proceedings of the U.S. National Academies of Science 99 (supp. 3), 7267–7274 (2002)CrossRefMathSciNetGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2004

Authors and Affiliations

  • David Hales
    • 1
  • Bruce Edmonds
    • 1
  1. 1.Centre for Policy ModellingManchester Metropolitan UniversityManchesterUK

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