Imperfect Norm Enforcement in Stochastic Environments: An Analysis of Efficiency and Cost Tradeoffs

  • Moser Silva Fagundes
  • Sascha Ossowski
  • Felipe Meneguzzi
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 8864)

Abstract

In heterogeneous multiagent systems, agents might interfere with each other either intentionally or unintentionally, as a side-effect of their activities. One approach to coordinating these agents is to restrict their activities by means of social norms whose compliance ensures certain system properties, or otherwise results in sanctions to violating agents. While most research on normative systems assumes a deterministic environment and norm enforcement mechanism, we formalize a normative system within an environment whereby agent actions have stochastic outcomes and norm enforcement follows a stochastic model in which stricter enforcement entails higher cost. Within this type of system, we analyze the tradeoff between norm enforcement efficiency (measured in number of norm violations) and its cost considering a population of norm-aware self-interested agents capable of building plans to maximize their expected utilities. Finally, we validate our analysis empirically through simulations in a representative scenario.

Keywords

NMDP MDP Stochastic Norm Enforcement 

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References

  1. 1.
    Ågotnes, T., van der Hoek, W., Wooldridge, M.: Normative system games. In: Durfee, E.H., Yokoo, M., Huhns, M.N., Shehory, O. (eds.) AAMAS, pp. 881–888. IFAAMAS (2007)Google Scholar
  2. 2.
    Bellman, R.E.: Dynamic Programming. Dover Publications, Incorporated (2003)Google Scholar
  3. 3.
    Boutilier, C., Dean, T., Hanks, S.: Decision-Theoretic Planning: Structural Assumptions and Computational Leverage. J. Artif. Intell. Res. (JAIR) 11, 1–94 (1999)MATHMathSciNetGoogle Scholar
  4. 4.
    Castelfranchi, C., Dignum, F., Jonker, C.M., Treur, J.: Deliberative Normative Agents: Principles and Architecture. In: Jennings, N.R., Lespérance, Y. (eds.) ATAL 1999. LNCS, vol. 1757, pp. 364–378. Springer, Heidelberg (2000)CrossRefGoogle Scholar
  5. 5.
    Fagundes, M.S.: Sequential Decision Making in Normative Environments. Ph.D. thesis, Universidad Rey Juan Carlos (2012)Google Scholar
  6. 6.
    Fagundes, M.S., Billhardt, H., Ossowski, S.: Reasoning about Norm Compliance with Rational Agents. In: Coelho, H., Studer, R., Wooldridge, M. (eds.) ECAI. Frontiers in Artificial Intelligence and Applications, vol. 215, pp. 1027–1028. IOS Press (2010)Google Scholar
  7. 7.
    Fagundes, M.S., Ossowski, S., Luck, M., Miles, S.: Using Normative Markov Decision Processes for evaluating electronic contracts. AI Commun. 25(1), 1–17 (2012)MathSciNetGoogle Scholar
  8. 8.
    Howard, R.A.: Dynamic Programming and Markov Processes. The M.I.T. Press (1960)Google Scholar
  9. 9.
    Jones, A.J.I., Sergot, M.: On the characterisation of law and computer systems: the normative systems perspective. In: Deontic Logic in Computer Science: Normative System Specification. Wiley Professional Computing Series, pp. 275–307. Wiley (1993)Google Scholar
  10. 10.
    Modgil, S., Faci, N., Meneguzzi, F.R., Oren, N., Miles, S., Luck, M.: A framework for monitoring agent-based normative systems. In: Sierra, C., Castelfranchi, C., Decker, K.S., Sichman, J.S. (eds.) AAMAS (1), pp. 153–160. IFAAMAS (2009)Google Scholar
  11. 11.
    Nash Jr, J.F.: Equilibrium points in n-person games. Proceedings of the National Academy of Sciences 36, 48–49 (1950)MATHMathSciNetCrossRefGoogle Scholar
  12. 12.
    Omicini, A., Ossowski, S., Ricci, A.: Coordination infrastructures in the engineering of multiagent systems. In: Bergenti, F., Gleizes, M.P., Zambonelli, F. (eds.) Methodologies and Software Engineering for Agent Systems: The Agent-Oriented Software Engineering Handbook, Multiagent Systems, Artificial Societies, and Simulated Organizations, vol. 11, ch. 14, pp. 273–296. Kluwer Academic Publishers (2004)Google Scholar
  13. 13.
    Puterman, M.L., Shin, M.C.: Modified Policy Iteration Algorithms for Discounted Markov Decision Problems. Management Science 24, 1127–1137 (1978)MATHMathSciNetCrossRefGoogle Scholar
  14. 14.
    Schumacher, M., Ossowski, S.: The governing environment. In: Weyns, D., Van Dyke Parunak, H., Michel, F. (eds.) E4MAS 2005. LNCS (LNAI), vol. 3830, pp. 88–104. Springer, Heidelberg (2006)CrossRefGoogle Scholar
  15. 15.
    Tennenholtz, M.: On social constraints for rational agents. Computational Intelligence 15(4), 367–383 (1999)CrossRefGoogle Scholar
  16. 16.
    Ummels, M., Wojtczak, D.: The complexity of nash equilibria in stochastic multiplayer games. Logical Methods in Computer Science 7(3) (2011)Google Scholar

Copyright information

© Springer International Publishing Switzerland 2014

Authors and Affiliations

  • Moser Silva Fagundes
    • 1
  • Sascha Ossowski
    • 2
  • Felipe Meneguzzi
    • 3
  1. 1.Federal Institute of Education, Science and Technology Sul-Rio-Grandense (IFSul)CharqueadasBrazil
  2. 2.Centre for Intelligent Information Technologies (CETINIA)University Rey Juan Carlos (URJC)MóstolesSpain
  3. 3.School of InformaticsPontifical Catholic University of Rio Grande do Sul (PUCRS)Porto AlegreBrazil

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