Aspects of Active Norm Learning and the Effect of Lying on Norm Emergence in Agent Societies

  • Bastin Tony Roy Savarimuthu
  • Rexy Arulanandam
  • Maryam Purvis
Part of the Lecture Notes in Computer Science book series (LNCS, volume 7047)


Norms have facilitated smoother functioning in human societies. In the field of normative multi-agent systems researchers are interested in investigating how the concept of social norms can be used to facilitate social order in electronic agent societies. In this context, the area of norm emergence has attracted a lot of interest among researchers. The objectives of this paper are two-fold. First, we discuss the norm learning approaches in agent societies and discuss the three aspects of active norm learning (experiential, observational and communication-based learning) in agent societies. Using an example we demonstrate the usefulness of combining these three aspects of norms learning. Second, we investigate the effect of the presence of liars in an agent society on norm emergence. Agents that lie distort truth when they are asked about the norm in an agent society. We show that lying has deleterious effect on norm emergence. In particular, using simulations we identify conditions under which the norms that have emerged in a society can be sustained in the presence of liars.


Multiagent System Convergence Criterion Agent Society Observational Learning Deontic Logic 
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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Copyright information

© Springer-Verlag Berlin Heidelberg 2011

Authors and Affiliations

  • Bastin Tony Roy Savarimuthu
    • 1
  • Rexy Arulanandam
    • 1
  • Maryam Purvis
    • 1
  1. 1.University of Otago, DunedinDunedinNew Zealand

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