Exploring the Generality of Norms in Multi-Agent Systems


  • Jhonatan Alves Federal University of Santa Catarina, Brazil
  • Jomi Fred Hübner Federal University of Santa Catarina, Brazil
  • Jerusa Marchi Federal University of Santa Catarina, Brazil




Normative Multi-agent Systems, Norms Generality, Goal Satisfiability, Conflicts Occurrence, Norms Creation


Norms are useful tools to regulate autonomous agents, and their generality is the focus of this paper. The generality of norms refers to the extent of behaviors the norms are capable of regulating. While very specific norms tend to be inefficient to avoid undesirable behaviors (since they are rarely activated), very general norms tend to limit excessively the options of the agents (since they are activated too often) hindering them to achieve the system goal. Therefore, a norm that efficiently regulates the agents should have a balanced generality, being neither too specific nor too general. Therefore, we consider that exploring the generality of norms is a fundamental key to obtaining efficient norms. However, the evaluation of their generality usually considers every behavior they regulate. Since it is likely an unfeasible task, in this paper, we investigate alternatives to estimate the norms generality from their syntactic characteristics. Based on these characteristics, we obtain different sequences of norms that vary, approximately, from the most specific to the most general. We assume thus that norms with a balanced generality are more easily found considering these orderings. Therefore, it is relevant to understand the impact of the syntactical characteristics in ordering the norms. In this context, we found out how different alternatives organize the norms space. This result is particularly useful for the development of algorithms for searching efficient norms that, through different strategies, may exploit how norms space is arranged and may be pruned.


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How to Cite

Alves, J., Hübner, J. F. ., & Marchi, J. (2023). Exploring the Generality of Norms in Multi-Agent Systems. Inteligencia Artificial, 26(72), 60–80. https://doi.org/10.4114/intartif.vol26iss72pp60-80

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