Abstract
Multi-Agent Systems (MAS) have been successfully applied in industry for their ability to address complex, distributed problems, especially in IoT-based systems. Their efficiency in achieving given objectives and meeting design requirements is strongly dependent on the MAS organization during the engineering process of an application-specific MAS. To design a MAS that can achieve given goals, available methods rely on the designer’s knowledge of the deployment environment. However, high complexity and low readability in some deployment environments make the application of these methods to be costly or raise safety concerns. In order to ease the MAS organization design regarding those concerns, we introduce an original Assisted MAS Organization Engineering Approach (AOMEA). AOMEA relies on combining a Multi-Agent Reinforcement Learning (MARL) process with an organizational model to suggest relevant organizational specifications to help in MAS engineering.
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Notes
- 1.
Additional explanation and the examples discussed using PRAHOM PettingZoo wrapper are available at https://github.com/julien6/omarl_experiments?tab=readme-ov-file#tutorial-predator-prey-with-communication.
References
Beynier, A., et al.: DEC-MDP/POMDP, chap. 9, pp. 277–318. John Wiley & Sons, Ltd. (2013). https://doi.org/10.1002/9781118557426.ch9, https://onlinelibrary.wiley.com/doi/abs/10.1002/9781118557426.ch9
Cernuzzi, L., Molesini, A., Omicini, A.: The Gaia methodology process. In: Cossentino, M., Hilaire, V., Molesini, A., Seidita, V. (eds.) Handbook on Agent-Oriented Design Processes, pp. 141–172. Springer, Heidelberg (2014). https://doi.org/10.1007/978-3-642-39975-6_6
Yu, C., et al.: The surprising effectiveness of PPO in cooperative, multi-agent games (2022)
Ferber, J., Gutknecht, O., Michel, F.: From agents to organizations: an organizational view of multi-agent systems. In: Giorgini, P., Müller, J.P., Odell, J. (eds.) AOSE 2003. LNCS, vol. 2935, pp. 214–230. Springer, Heidelberg (2004). https://doi.org/10.1007/978-3-540-24620-6_15
Group, T.C.W.: TTCP cage challenge 3 (2022). https://github.com/cage-challenge/cage-challenge-3
Hubner, J.F., et al.: Developing organised multiagent systems using the moise+ model: programming issues at the system and agent levels. Int. J. Agent-Oriented Software Eng. 370 (2007). https://doi.org/10.1504/ijaose.2007.016266
Jamont, J.P., Occello, M.: Meeting the challenges of decentralized embedded applications using multi-agent systems. Int. J. Agent Oriented Software Eng. 5(1), 22–67 (2015). https://doi.org/10.1504/IJAOSE.2015.078435, https://hal.univ-grenoble-alpes.fr/hal-01265071
Kazhdan, D., Shams, Z., Lio, P.: MARLeME: a multi-agent reinforcement learning model extraction library. In: 2020 International Joint Conference on Neural Networks (IJCNN), pp. 1–8 (2020) https://doi.org/10.1109/IJCNN48605.2020.9207564
Keogh, K., Sonenberg, L.: Designing multi-agent system organisations for flexible runtime behaviour. Appl. Sci. 10(15) (2020). https://www.mdpi.com/2076-3417/10/15/5335
Kott, A.: Autonomous Intelligent Cyber Defense Agent (AICA): A Comprehensive Guide, vol. 87. Springer, Cham (2023). https://doi.org/10.1007/978-3-031-29269-9
Bakliwal, K., et al.: A multi agent system architecture to implement collaborative learning for social industrial assets. IFAC-PapersOnLine 51(11), 1237–1242 (2018).https://doi.org/10.1016/j.ifacol.2018.08.421, https://www.sciencedirect.com/science/article/pii/S2405896318315477, iFAC Symp. on Information Control Problems in Manufacturing INCOM
Lowe, R., et al.: Multi-agent actor-critic for mixed cooperative-competitive environments. In: Neural Information Processing Systems (NIPS) (2017)
Mefteh, W., Migeon, F., Gleizes, M.-P., Gargouri, F.: ADELFE 3.0 design, building adaptive multi agent systems based on simulation a case study. In: Núñez, M., Nguyen, N.T., Camacho, D., Trawiński, B. (eds.) ICCCI 2015. LNCS (LNAI), vol. 9329, pp. 19–28. Springer, Cham (2015). https://doi.org/10.1007/978-3-319-24069-5_2
Oliehoek, F.A., Amato, C.: A Concise Introduction to Decentralized POMDPs. Springer Briefs in Intelligent Systems. Springer, Cham (2016).https://doi.org/10.1007/978-3-319-28929-8,
Picard, G., Hübner, J.F., Boissier, O., Gleizes, M.P.: Réorganisation et auto-organisation dans les systèmes multi-agents. In: Journées Francophones sur les Systèmes Multi-Agents (JFSMA’09), p. 89 (2009)
Răileanu, S., Borangiu, T.: A review of multi-agent systems used in industrial applications. In: Borangiu, T., Trentesaux, D., Leitão, P. (eds.) Service Oriented, Holonic and Multi-Agent Manufacturing Systems for Industry of the Future. pp. 3–22. Springer, Cham (2023). https://doi.org/10.1007/978-3-031-24291-5_1
Schön, S., et al.: Integration of multi-fidelity models with agent-based simulation for system of systems. In: AIAA Aviation 2021 Forum, p. 2996 (2021)
Sims, M., Corkill, D., Lesser, V.: Automated organization design for multi-agent systems. Auton. Agents Multi-agent Syst. 16(2), 151–185 (2008). https://doi.org/10.1007/s10458-007-9023-8
Terry, J., et al.: PettingZoo: gym for multi-agent reinforcement learning. In: Advances in Neural Information Processing Systems, vol. 34, pp. 15032–15043 (2021)
Tonghan Wang et. al.: Roma: multi-agent reinforcement learning with emergent roles (2020)
Tošić, P.T., Vilalta, R.: A unified framework for reinforcement learning, co-learning and meta-learning how to coordinate in collaborative multi-agent systems. Procedia Comput. Sci. 1(1), 2217–2226 (2010). https://doi.org/10.1016/j.procs.2010.04.248, https://www.sciencedirect.com/science/article/pii/S1877050910002498, iCCS 2010
Wooldridge, M., et al.: The Gaia methodology for agent-oriented analysis and design. Auton. Agent. Multi-Agent Syst. 3, 285–312 (2000)
Zheng, L., et al.: MAgent: a many-agent reinforcement learning platform for artificial collective intelligence. In: Proceedings of the AAAI Conference on Artificial Intelligence 32(1) (2018). https://doi.org/10.1609/aaai.v32i1.11371, https://ojs.aaai.org/index.php/AAAI/article/view/11371
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Soulé, J., Jamont, JP., Occello, M., Traonouez, LM., Théron, P. (2024). A MARL-Based Approach for Easing MAS Organization Engineering. In: Maglogiannis, I., Iliadis, L., Macintyre, J., Avlonitis, M., Papaleonidas, A. (eds) Artificial Intelligence Applications and Innovations. AIAI 2024. IFIP Advances in Information and Communication Technology, vol 714. Springer, Cham. https://doi.org/10.1007/978-3-031-63223-5_24
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