面向自主分散式学习制造系统的基于状态的势博弈分布式Stackelberg策略
Distributed Stackelberg Strategies in State-based Potential Games for Autonomous Decentralized Learning Manufacturing Systems
AI总结：
本文提出融合势博弈与Stackelberg博弈的DS2-SbPG结构，以完全分布式方式解决分散式制造系统的多目标优化问题，证明其收敛性并通过实验验证其降功耗、提性能的效果。
摘要：
本文提出了一种用于自主优化存在多目标优化挑战的分散式制造系统的新型博弈结构，即基于状态的势博弈分布式Stackelberg策略（Distributed Stackelberg Strategies in State-Based Potential Games, DS2-SbPG）。DS2-SbPG融合了势博弈与Stackelberg博弈，提升了势博弈的协作权衡能力，同时增强了Stackelberg博弈的多目标优化处理能力。值得注意的是，所有训练过程均以完全分布式的方式执行。DS2-SbPG通过消除自学习领域中为单个参与者设置组合目标优化函数的复杂性，为找到目标间的最优权衡提供了可行方案，尤其适用于子系统间存在多样且大量目标的真实工业场景。我们进一步证明DS2-SbPG构成动态势博弈，具备相应的收敛保证。在实验室规模测试平台上开展的实验验证凸显了DS2-SbPG及其两个变体（面向单领导者-追随者的DS2-SbPG、面向多领导者-追随者的Stack DS2-SbPG）的有效性。结果显示，该方法显著降低了功耗，提升了整体性能，表明DS2-SbPG在实际应用中具备潜力。
英文摘要：
This article describes a novel game structure for autonomously optimizing decentralized manufacturing systems with multi-objective optimization challenges, namely Distributed Stackelberg Strategies in State-Based Potential Games (DS2-SbPG). DS2-SbPG integrates potential games and Stackelberg games, which improves the cooperative trade-off capabilities of potential games and the multi-objective optimization handling by Stackelberg games. Notably, all training procedures remain conducted in a fully distributed manner. DS2-SbPG offers a promising solution to finding optimal trade-offs between objectives by eliminating the complexities of setting up combined objective optimization functions for individual players in self-learning domains, particularly in real-world industrial settings with diverse and numerous objectives between the sub-systems. We further prove that DS2-SbPG constitutes a dynamic potential game that results in corresponding converge guarantees. Experimental validation conducted on a laboratory-scale testbed highlights the efficacy of DS2-SbPG and its two variants, such as DS2-SbPG for single-leader-follower and Stack DS2-SbPG for multi-leader-follower. The results show significant reductions in power consumption and improvements in overall performance, which signals the potential of DS2-SbPG in real-world applications.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
