用多智能体强化学习辅助设计复杂系统的组织结构。
A MARL-based Approach for Easing MAS Organization Engineering
- 结合MARL与组织模型生成组织方案
- 降低高复杂度环境下的设计成本与风险
- 适合需要高效组织设计的工业级系统
多智能体系统（MAS）在工业中被成功应用于解决复杂分布式问题，尤其在基于物联网的系统中。其在达成目标和满足设计要求方面的效率，高度依赖于应用特定的MAS组织设计过程。现有方法依赖设计者对部署环境的知识，但在高复杂度、低可读性的环境中，这些方法成本高昂或引发安全问题。为此，本文提出一种新的辅助多智能体系统组织工程方法（AOMEA），通过将多智能体强化学习（MARL）过程与组织模型相结合，建议相关组织规范，以辅助多智能体系统工程设计。
原文摘要 · Abstract (English)
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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