标题： 基于MARL的MAS组织工程缓解方法 显示英文标题

标题： A MARL-based Approach for Easing MAS Organization Engineering

摘要： 多智能体系统（MAS）因其解决复杂分布式问题的能力，在工业领域得到了成功应用，尤其是在基于物联网的系统中。 它们在实现既定目标和满足设计要求方面的效率，在特定应用多智能体系统的工程过程中，高度依赖于MAS的组织方式。 为了设计能够实现既定目标的MAS，现有的方法依赖于设计者对部署环境的了解。 然而，一些部署环境中高复杂性和低可读性使得这些方法的应用成本高昂或引发安全顾虑。 为了解决这些问题，我们提出了一种原创的辅助多智能体系统组织工程方法（AOMEA）。 AOMEA通过结合多智能体强化学习（MARL）过程与组织模型，来建议相关的组织规范，以协助MAS工程。 显示英文摘要

摘要： 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.