most citedTransfer learning of state-based potential games for process optimization in decentralized manufacturing systems

5 citations · 5 across the 1 of their papers we have counts for

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5 papers

cs.LG20255 cited

Transfer learning of state-based potential games for process optimization in decentralized manufacturing systems

Steve Yuwono, Dorothea Schwung, Andreas Schwung

This paper presents a novel online transfer learning approach in state-based potential games (TL-SbPGs) for distributed self-optimization in manufacturing systems. The approach tar…

cs.AI2025

Real Time Self-Tuning Adaptive Controllers on Temperature Control Loops using Event-based Game Theory

Steve Yuwono, Muhammad Uzair Rana, Dorothea Schwung +1

This paper presents a novel method for enhancing the adaptability of Proportional-Integral-Derivative (PID) controllers in industrial systems using event-based dynamic game theory,…

cs.AI2024

Self-optimization in distributed manufacturing systems using Modular State-based Stackelberg Games

Steve Yuwono, Ahmar Kamal Hussain, Dorothea Schwung +1

In this study, we introduce Modular State-based Stackelberg Games (Mod-SbSG), a novel game structure developed for distributed self-learning in modular manufacturing systems. Mod-S…

cs.GT2024

Distributed Stackelberg Strategies in State-based Potential Games for Autonomous Decentralized Learning Manufacturing Systems

Steve Yuwono, Dorothea Schwung, Andreas Schwung

This article describes a novel game structure for autonomously optimizing decentralized manufacturing systems with multi-objective optimization challenges, namely Distributed Stack…

cs.LG2024

Gradient-based Learning in State-based Potential Games for Self-Learning Production Systems

Steve Yuwono, Marlon Löppenberg, Dorothea Schwung +1

In this paper, we introduce novel gradient-based optimization methods for state-based potential games (SbPGs) within self-learning distributed production systems. SbPGs are recogni…