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This project explores the potential of Generative Autonomous Agents and Multiagent Systems (GAAMAS) for social simulation. It aims to better understand how these artificial entities, powered by Large Language Models (LLMs), interact, make decisions, adapt to others' behaviour, and simulate human reasoning, particularly in strategic contexts inspired by Game Theory. This project will contribute to assessing the current capabilities and limitations of GAAMAS and to proposing concrete avenues for improving their coherence and realism in social simulations.

Create and manipulate RO-Crate and Provenance Run RO-Crate metadata for workflows. Generate RO-Crate metadata files for CWL workflows.

Repository contaning the original code of IMPACT algorithm, an interpretable model for ordinal predictions with multi-class outputs"

Community Detection Attack against Collaborative Learning-based Recommender Systems

Matériel pour l'atelier ANF TDM 2022 "Librairies Python et Services Web pour la reconnaissance d’entités nommées et la résolution de toponymes"