| name | coordinating-from-memory-graph-structured-experien |
| description | Skill generated from arXiv paper 2607.19985: Coordinating from Memory: Graph-Structured Experience Reuse for Multi-Agent Adaptation in Dynamic Manufacturing |
| metadata | {"arxiv":{"id":"2607.19985","title":"Coordinating from Memory: Graph-Structured Experience Reuse for Multi-Agent Adaptation in Dynamic Manufacturing","authors":["Chengxiao Dai","Zhanhui Lin","Zhaokun Yan","Youyang Ni","Chenjun Lei","Luyan Zhang"],"published":"2026-07-22","categories":["cs.AI"],"url":"https://arxiv.org/abs/2607.19985","utility":1}} |
Coordinating from Memory: Graph-Structured Experience Reuse for Multi-Agent Adaptation in Dynamic Manufacturing
arXiv: 2607.19985
Published: 2026-07-22
Authors: Chengxiao Dai, Zhanhui Lin, Zhaokun Yan, Youyang Ni, Chenjun Lei, Luyan Zhang
Categories: cs.AI
Utility: 1.00
Key Innovation
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 coordina...
Potential Application
This paper presents advancements that could be applied to enhance agent capabilities in the areas of cs.AI.
References
