REVIEW article
Front. Artif. Intell.
Sec. Machine Learning and Artificial Intelligence
Norm Mining, Identification, and Detection: A Systematic Literature Review
Provisionally accepted
- 1Universite du Luxembourg, Esch-sur-Alzette, Luxembourg
- 2Universite de Technologie de Belfort-Montbeliard, Belfort, France
- 3Luxembourg Institute of Science and Technology, Esch-sur-Alzette, Luxembourg
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This paper presents a systematic literature review on norm identification in multi-agent systems. Norms play a crucial role in guiding agent behavior, ensuring cooperation, and resolving conflicts. By analyzing 35 selected studies, we categorize methods for detecting, synthesizing, and adapting norms in multi-agent systems. We also examine their effectiveness in dynamic and uncertain environments. The findings highlight gaps in current approaches, including scalability, adaptability, and real-world applicability. Future directions emphasize the integration of Large Language Models, testing in complex environments, and fostering interdisciplinary collaboration to advance socially aware autonomous systems.
Keywords: Data Mining, Norm identification, normative systems, Norms, Systematic Literature Review
Received: 10 Sep 2025; Accepted: 09 Feb 2026.
Copyright: © 2026 ALCARAZ, Mualla, Bhattacharya, Tchappi, de Wit and Najjar. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) or licensor are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.
* Correspondence: Benoît ALCARAZ
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