# Norm mining, identification, and detection: a systematic literature review

## TL;DR

This paper reviews methods for identifying and managing norms in multi-agent systems, highlighting current limitations and future research directions.

## Contribution

A systematic review of norm identification techniques in multi-agent systems, revealing gaps and suggesting future research areas.

## Key findings

- Current norm identification methods struggle with scalability and adaptability in dynamic environments.
- Integration of Large Language Models is proposed as a promising future direction for norm mining.
- Interdisciplinary collaboration is emphasized to improve real-world applicability of normative systems.

## Abstract

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.