Learning from Experience to Generate New Regulations

  • Jan Koeppen
  • Maite Lopez-Sanchez
  • Javier Morales
  • Marc Esteva
Part of the Lecture Notes in Computer Science book series (LNCS, volume 6541)


Both human and multi-agent societies are prone to best function with the inclusion of regulations. Human societies have developed jurisprudence as the theory and philosophy of law. Within it, utilitarianism has the view that laws should be crafted so as to produce the best consequences. Following this same objective, we propose an approach to enhance a multi-agent system with a regulatory authority that generates new regulations –norms– based on the outcome of previous experiences. These regulations are learned by applying a machine learning technique (based on Case-Based Reasoning) that uses previous experiences to solve new problems. As a scenario to evaluate this innovative proposal, we use a simplified version of a traffic simulation scenario, where agents move within a road junction. Gathered experiences can then be easily mapped into regular traffic rules that, if followed, happen to be effective in avoiding undesired situations —and promoting desired ones. Thus, we can conclude that our approach can be successfully used to create new regulations for those multi-agent systems that accomplish two general conditions: to be able to continuously gather and evaluate experiences from its regular functioning; and to be characterized in such a way that similar social situations require similar regulations.


Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.


  1. 1.
    Davis, N.A.: Contemporary deontology. In: Singer, P. (ed.) A Companion to Ethics, pp. 205–218. Blackwell, Malden (1993)Google Scholar
  2. 2.
    Mill, J.S.: Utilitarianism. Parker, Son, and Bourn, London (1863)Google Scholar
  3. 3.
    McCarty, T.: Reflections on Taxman: An Experiment in Artificial Intelligence and Legal Reasoning. Harvard Law Review, 837–93 (1977)Google Scholar
  4. 4.
    Busoniu, L., Babuska, R., de Schutter, B.: A comprehensive survey of multiagent reinforcement learning. IEEE Transactions on Systems, Man, and Cybernetics, Part C: Applications and Reviews 38(2), 156–172 (2008)CrossRefGoogle Scholar
  5. 5.
    Zhang, C., Abdallah, S., Lesser, V.: Integrating organizational control into multi-agent learning. In: Aut. Agents and Multiagent Systems, pp. 757–764 (2009)Google Scholar
  6. 6.
    Boella, G., van der Torre, L.: Regulative and constitutive norms in normative multiagent systems. In: Proceedings of KR 2004, pp. 255–265 (2004)Google Scholar
  7. 7.
    Campos, J., López-Sánchez, M., Esteva, M.: Multi-Agent System adaptation in a Peer-to-Peer scenario. In: ACM Symposium on Applied Computing - Agreement Technologies Track, pp. 735–739 (2009)Google Scholar
  8. 8.
    Artikis, A., Kaponis, D., Pitt, J.: Dynamic Specifications of Norm-Governed Systems. In: Multi-Agent Systems: Semantics and Dynamics of Organisational Models (2009)Google Scholar
  9. 9.
    Savarimuthu, B., Cranefield, S., Purvis, M., Purvis, M.: Role model based mechanism for norm emergence in artificial agent societies. In: Sichman, J.S., Padget, J., Ossowski, S., Noriega, P. (eds.) COIN 2007. LNCS (LNAI), vol. 4870, pp. 203–217. Springer, Heidelberg (2008)CrossRefGoogle Scholar
  10. 10.
    Griffiths, N., Luck, M.: Norm Emergence in Tag-Based Cooperation. In: The Ninth International Workshop on Coordination, Organization, Institutions and Norms in Multi-Agent Systems, pp. 79–86 (2010)Google Scholar
  11. 11.
    Kota, R., Gibbins, N., Jennings, N.: Decentralised structural adaptation in agent organisations. In: AAMAS Workshop Organised Adaptation in MAS (2008)Google Scholar
  12. 12.
    Shoham, Y., Tennenholtz, M.: On social laws for artificial agent societies: off-line design. Journal of Artificial Intelligence 73(1-2), 231–252 (1995)CrossRefGoogle Scholar
  13. 13.
    van der Hoek, W., Roberts, M., Wooldridge, M.: Social laws in alternating time: Effectiveness, feasibility, and synthesis. Synthese 1, 156 (2007)MathSciNetMATHGoogle Scholar
  14. 14.
    Agotnes, T., Wooldridge, M.: Optimal Social Laws. In: Proceedings of he Ninth International Conference on Autonomous Agents and Multiagent Systems, pp. 667–674 (2010)Google Scholar
  15. 15.
    Christelis, G., Rovatsos, M.: Automated norm synthesis in an agent-based planning enviroment. In: Autonomous Agents and Multiagent Systems (AAMAS), pp. 161–168 (2009)Google Scholar
  16. 16.
    Christelis, G., Rovatsoshas, M., Petrick, R.: Exploiting Domain Knowledge to Improve Norm Synthesis. In: Proceedings of he Ninth International Conference on Autonomous Agents and Multiagent Systems, pp. 831–838 (2010)Google Scholar
  17. 17.
    Modgil, S., Faci, N., Meneguzzi, F., Oren, N., Miles, S., Luck, M.: A framework for monitoring agent-based normative systems. In: Autonomous Agents and Multiagent Systems (AAMAS), pp. 153–160 (2009)Google Scholar
  18. 18.
    Dunkel, J., Fernandez, A., Ortiz, R., Ossowski, S.: Event-driven architecture for decision support in traffic management systems. In: IEEE Intelligent Transportation Systems Conf., pp. 7–13 (2008)Google Scholar
  19. 19.
    North, M., Howe, T., Collier, N., Vos, J.: Repast Simphony Runtime System. In: Agent Conf. Generative Social Processes, Models, and Mechanisms (2005)Google Scholar
  20. 20.
    Aamodt, A., Plaza, E.: Case-based reasoning: Foundational issues, methodological variations, and system approaches. AI Commun. 7(1), 39–59 (1994)Google Scholar
  21. 21.
    Powell, J.H., Hauff, B.M., Hastings, J.D.: Evaluating the effectiveness of exploration and accumulated experience in automatic case elicitation. In: Muñoz-Ávila, H., Ricci, F. (eds.) ICCBR 2005. LNCS (LNAI), vol. 3620, pp. 397–407. Springer, Heidelberg (2005)CrossRefGoogle Scholar
  22. 22.
    Koeppen, J.F.: Norm Generation in Multi-Agent Systems (master thesis). Univ. of Barcelona (2009)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2011

Authors and Affiliations

  • Jan Koeppen
    • 1
  • Maite Lopez-Sanchez
    • 1
  • Javier Morales
    • 1
  • Marc Esteva
    • 2
  1. 1.MAiA dept.Universitat de BarcelonaSpain
  2. 2.Artificial Intelligence Research Institute (IIIA-CSIC)Spain

Personalised recommendations