Source Norms and Self-regulated Institutions

  • Rossella Rubino
  • Giovanni Sartor
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 4884)


In this paper we shall focus on an important class of constitutive norms, which we shall call source-norms, namely those norms establishing what norms, on basis of what properties, validly belong to a normative system. Institutions including their own source-norms – here called Self-Regulated Institutions – are able to incorporate dynamically and autonomously new norms in their normative systems. After describing these concepts, we shall present a formal model of source-norms built by exploiting the PRATOR system for defeasible argumentation and we shall try to apply it to electronic institutions.


self-regulated institutions source-norms recognition rules normative production 


Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.


  1. 1.
    Kelsen, H.: Reine Rechtslehre. Einleitung in die rechtswissenschaftliche Problematik. Franz Deuticke (1934)Google Scholar
  2. 2.
    Hart, H.L.A.: The Concept of Law. Oxford University Press, Oxford (1997)Google Scholar
  3. 3.
    Kelsen, H.: The Pure Theory of Law. University of California Press, Berkeley (1967)Google Scholar
  4. 4.
    Prakken, H., Sartor, G.: Argument-based extended logic programming with defeasible priorities. Journal of Applied Non-Classical Logics 7, 25–75 (1997)MATHMathSciNetGoogle Scholar
  5. 5.
    Yoshino, H.: The Systematization of Legal Metainference. In: Proceedings of the Fifth International Conference of Artificial Intelligence and Law (ICAIL). ACM, New York (1995)Google Scholar
  6. 6.
    Hernandez Marín, R., Sartor, G.: Time and norms: A formalisation in the event-calculus. In: Proceedings of the Seventh International Conference on Artificial Intelligence and Law (ICAIL), pp. 90–100. ACM, New York (1999)CrossRefGoogle Scholar
  7. 7.
    Sartor, G.: Legal validity: An inferential analysis. Ratio Juris (forthcoming, 2008)Google Scholar
  8. 8.
    Excelente-Toledo, C.B., Jennings, N.R.: The dynamic selection of coordination mechanisms. Autonomous Agents and Multi-Agents Systems 9, 55–85 (2004)CrossRefGoogle Scholar
  9. 9.
    Lopez y Lopez, F., Luck, M., d’Inverno, M.: Constraining autonomy through norms. In: Proceedings of the First International Joint Conference on Autonomous Agents and Multi-agent Systems (AAMAS 2002) - Session 6B: social order, pp. 674–681. ACM Press, New York (2002)CrossRefGoogle Scholar
  10. 10.
    Horling, B., Benyo, B., Lesser, V.: Using self-diagnosis to adapt organizational structures. In: Proceedings of the Fifth International Conference on Autonomous Agents, pp. 529–536. ACM Press, New York (2001)CrossRefGoogle Scholar
  11. 11.
    Gasser, L., Ishida, T.: A dynamic organizational architecture for adaptive problem solving. In: Proceedings of the Ninth National Conference on Artificial Intelligence (AAAI 1991), American Association for Artificial Intelligence, pp. 185–190. AAAI Press/MIT Press (1991)Google Scholar
  12. 12.
    Ishida, T., Gassend, L., Yokoo, M.: Organization self-design of distributed production systems. IEEE Transactions on Knowledge and Data Engineering 4, 123–134 (1992)CrossRefGoogle Scholar
  13. 13.
    Hubner, F.J., Simao Sichman, J., Boissier, O.: Using the MOISE+ for a Cooperative Framework of MAS reorganisation. In: Bazzan, A.L.C., Labidi, S. (eds.) SBIA 2004. LNCS (LNAI), vol. 3171, pp. 506–515. Springer, Heidelberg (2004)Google Scholar
  14. 14.
    Conte, R.: Emergent (info)institutions. Journal of Cognitive System Research 2, 97–110 (2001)CrossRefGoogle Scholar
  15. 15.
    Walker, A., Wooldridge, M.: Understanding the emergence of conventions in multi-agent systems. In: Lesser, V., Gasser, L. (eds.) Proceedings of the First International Conference on Multi-agent Systems (ICMAS 1995), pp. 384–389. AAAI Press/MIT Press (1995)Google Scholar
  16. 16.
    Fitoussi, D., Tennenholtz, M.: Choosing social laws for multi-agent systems: Minimality and simplicity. Artificial Intelligence 119, 61–101 (2000)MATHCrossRefMathSciNetGoogle Scholar
  17. 17.
    Amgoud, L., Bodenstaff, L., Caminada, M., McBurney, P., Parsons, S., Prakken, H., van Veenen, J., Vreeswijk, G.: Deliverable d2.6 - final review and report on formal argumentation system. Technical report, ASPIC - Argumentation Service Platform with Integrated Components (2006)Google Scholar
  18. 18.
    Gordon, T.F.: Constructing arguments with a computational model of an argumentation scheme for legal rules. In: Proceedings of the Eleventh International Conference on Artificial Intelligence and Law (ICAIL-2007), pp. 117–121. ACM, New York (2007)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2008

Authors and Affiliations

  • Rossella Rubino
    • 1
  • Giovanni Sartor
    • 1
    • 2
  1. 1.CIRSFIDBolognaItaly
  2. 2.European University InstituteSan Domenico di FiesoleItaly

Personalised recommendations