发表机构
School of Computer Science, University of Sydney; Khoury College of Computer Sciences, Northeastern University; School of Computation, Information and Technology, Technical University of Munich; School of Life and Environmental Sciences, University of Sydney; College of Business and Economics, Australian National University（悉尼大学计算机科学学院; 美国东北大学库里计算机科学学院; 慕尼黑工业大学计算、信息与技术学院; 悉尼大学生命与环境科学学院; 澳大利亚国立大学商业与经济学院）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
针对动态制造中多智能体协调问题，提出图结构经验记忆（GSEM）框架，将历史协调事件编码为图，新干扰时通过图神经网络检索相似事件实现经验引导策略适应，实验表明其能有效减少完工时间和适应时间，验证了方法有效性和通用性。
AI 中文摘要
动态制造环境要求多智能体系统在诸如机器故障、紧急任务到达和加工时间变化等频繁操作干扰下有效协调。现有多智能体强化学习方法独立处理每个干扰事件，丢弃了可加速未来适应的宝贵协调经验。本文提出用于动态制造中多智能体协调的图结构经验记忆（GSEM）框架。该框架将历史协调事件编码为捕获任务依赖、机器状态和智能体间协作模式的异构关系图。新干扰出现时，基于图神经网络的检索机制识别结构相似的过去事件，实现经验引导的策略适应而非从头学习。在具有三种干扰类型的动态柔性作业车间调度基准测试上的实验表明，与最强的记忆增强基线相比，GSEM将完工时间减少4.1%-10.0%，适应时间减少33%-38%，且在更高干扰频率下优势增加。消融研究和跨干扰转移实验进一步验证了图结构编码和基于相似性检索的必要性，并证明了学习到的协调模式的跨干扰通用性。
英文摘要
Dynamic manufacturing environments require multi-agent systems to coordinate effectively under frequent operational disturbances such as machine failures, urgent job arrivals, and processing time variations. Existing multi-agent reinforcement learning approaches treat each disturbance episode independently, discarding valuable coordination experience that could accelerate future adaptation. In this paper, we propose a Graph-Structured Experiential Memory (GSEM) framework for multi-agent coordination in dynamic manufacturing. The framework encodes historical coordination episodes as heterogeneous relational graphs that capture task dependencies, machine states, and inter-agent collaboration patterns. When a new disturbance occurs, a graph neural network-based retrieval mechanism identifies structurally similar past episodes, enabling experience-guided policy adaptation rather than learning from scratch. Experiments on dynamic flexible job-shop scheduling benchmarks with three disturbance types show that GSEM reduces makespan by 4.1%-10.0% and adaptation time by 33%-38% compared to the strongest memory-augmented baseline, with the advantage increasing under higher disturbance frequency. Ablation studies and cross-disturbance transfer experiments further validate the necessity of graph-structured encoding and similarity-based retrieval and demonstrate the cross-disturbance generalizability of learned coordination patterns.
