A MARL-based Approach for Easing MAS Organization Engineering
This work addresses the problem of costly and unsafe MAS organization design for engineers in IoT-based systems, representing an incremental improvement over existing methods.
The paper tackles the challenge of designing efficient Multi-Agent Systems (MAS) in complex deployment environments by introducing AOMEA, an approach that combines Multi-Agent Reinforcement Learning with an organizational model to suggest organizational specifications, aiming to reduce costs and safety concerns.
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.