Synthesising Liberal Normative Systems

Javier Morales, Maite Lopez-Sanchez, Juan A. Rodriguez-Aguilar, Michael Woolridge, Wamberto Weber Vasconcelos

Research output: Chapter in Book/Report/Conference proceedingPublished conference contribution

25 Citations (Scopus)


Norms have been extensively studied to coordinate multi-agent systems,
and the literature has investigated two general approaches to
norm synthesis: off-line (synthesising norms at design-time) and
on-line (run-time synthesis). On-line synthesis is generally recognised
to be appropriate for open systems, where aspects of the
system remain unknown at design-time. In this paper we present
LION, an algorithm aimed at synthesising liberal normative systems.
LION’s normative systems respect the agents’ autonomy to
the greatest possible extent, constraining their behaviour when only
necessary to avoid undesirable system states. LION’s norm synthesis
is also driven by the need to construct compact normative systems.
The key to the success of LION in this multi-objective synthesis
process is that it learns about and exploits norm synergies. More
precisely, LION can learn when norms are either substitutable or
complementary. We show empirically that LION significantly outperforms
the state of the art by synthesising normative systems that
are more liberal while maintaining representation compactness.
Original languageEnglish
Title of host publicationAAMAS '15 Proceedings of the 2015 International Conference on Autonomous Agents and Multiagent Systems
Place of PublicationRichland
PublisherInternational Foundation for Autonomous Agents and Multiagent Systems
Number of pages9
ISBN (Print)978-1-4503-3413-6
Publication statusPublished - 4 May 2015
Event2015 International Conference on Autonomous Agents and Multiagent Systems - Istanbul, Turkey
Duration: 4 May 20158 May 2015


Conference2015 International Conference on Autonomous Agents and Multiagent Systems


  • norms
  • normative systems
  • on-line norm synthesis


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