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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.

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.

Python Generative Autonomous Agents and Multi-Agent Systems aims to evaluate the social behaviors of LLM-based agents.

Community Detection Attack against Collaborative Learning-based Recommender Systems

Privacy in Decentralized Learning of Recommender Systems against Community Detection Attack

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