Lazy Nested Monte Carlo Search for Coalition Structure Generation

Milo Roucairol, Jérôme Arjonilla, Abdallah Saffidine, Tristan Cazenave

2024

Abstract

This paper explores Monte-Carlo Search algorithms applied to Multiagent Systems (MAS), specifically focusing on the problem of Coalition Structure Generation (CSG). CSG is a NP-Hard problem consisting in partitioning agents into coalitions to optimize collective performance. Our study makes three contributions: (i) a novel action space representation tailored for CSG, (ii) a comprehensive comparative analysis of multiple algorithms, and the introduction of Lazy NMCS, (iii) a cutting-edge method that surpasses previous benchmarks. By outlining efficient coalition formation strategies, our findings offer insights for advancing MAS research and practical applications.

Paper Citation

in Harvard Style

Roucairol M., Arjonilla J., Saffidine A. and Cazenave T. (2024). Lazy Nested Monte Carlo Search for Coalition Structure Generation. In Proceedings of the 16th International Conference on Agents and Artificial Intelligence - Volume 2: ICAART; ISBN 978-989-758-680-4, SciTePress, pages 58-67. DOI: 10.5220/0012302300003636

in Bibtex Style

@conference{icaart24,
author={Milo Roucairol and Jérôme Arjonilla and Abdallah Saffidine and Tristan Cazenave},
title={Lazy Nested Monte Carlo Search for Coalition Structure Generation},
booktitle={Proceedings of the 16th International Conference on Agents and Artificial Intelligence - Volume 2: ICAART},
year={2024},
pages={58-67},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0012302300003636},
isbn={978-989-758-680-4},
}

in EndNote Style

TY - CONF

JO - Proceedings of the 16th International Conference on Agents and Artificial Intelligence - Volume 2: ICAART
TI - Lazy Nested Monte Carlo Search for Coalition Structure Generation
SN - 978-989-758-680-4
AU - Roucairol M.
AU - Arjonilla J.
AU - Saffidine A.
AU - Cazenave T.
PY - 2024
SP - 58
EP - 67
DO - 10.5220/0012302300003636
PB - SciTePress