A MARL-based Approach for Easing MAS Organization Engineering
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arXiv
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| Main Authors: | , , , , |
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| Format: | Preprint |
| Published: |
2025
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| _version_ | 1866915328801374208 |
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| author | Soulé, Julien Jamont, Jean-Paul Occello, Michel Traonouez, Louis-Marie Théron, Paul |
| author_facet | Soulé, Julien Jamont, Jean-Paul Occello, Michel Traonouez, Louis-Marie Théron, Paul |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2506_05437 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | A MARL-based Approach for Easing MAS Organization Engineering Soulé, Julien Jamont, Jean-Paul Occello, Michel Traonouez, Louis-Marie Théron, Paul Multiagent Systems Artificial Intelligence 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. |
| title | A MARL-based Approach for Easing MAS Organization Engineering |
| topic | Multiagent Systems Artificial Intelligence |
| url | https://arxiv.org/abs/2506.05437 |