A MARL-based Approach for Easing MAS Organization Engineering

TL;DR

This paper introduces AOMEA, a novel approach combining Multi-Agent Reinforcement Learning with organizational models to simplify the design of Multi-Agent Systems in complex, safety-critical environments.

Contribution

The paper presents AOMEA, an original method that leverages MARL and organizational models to assist in MAS organization engineering, addressing complexity and safety concerns.

Findings

AOMEA effectively suggests organizational specifications for MAS design.

The approach reduces complexity and improves safety in MAS deployment.

Experimental results demonstrate the method's practical benefits.

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.