2025

Terziyan, Vagan; Vitko, Oleksandra

Rapid evolution of Industry 4.0 and 5.0 demands intelligent, autonomous systems capable of making adaptive decisions in complex dynamic environments using advanced artificial intelligence and machine learning with respect to various contexts. Traditional approaches in both reinforcement learning (RL) and neural networks (NNs) often struggle with several critical challenges. Specifically, RL methods face difficulties in balancing conflicting objectives and quickly adapting to changing contexts, particularly in industrial applications where operational efficiency must be balanced with system maintenance. Similarly, traditional NNs lack the ability to incorporate contextual awareness, limiting their robustness and adaptability in real-world scenarios. This paper addresses these gaps by proposing several solutions. First, we introduce a context-aware balanced RL framework that integrates supervised learning principles into RL, enhancing the ability of intelligent agents to make balanced decisions that consider both external operational goals and internal “health” metrics. Second, we propose a Context-Aware NN architecture that incorporates contextual attributes, improving the network’s performance and relevance in dynamic environments. Third, we present a Self-Context-Aware NN architecture, which enhances model robustness by incorporating sample attribution analysis as a contextual attribute. Finally, we developed a context-aware digital vaccination framework for enhancing models’ robustness. Collectively, these approaches contribute to context-aware, adaptable systems for smart manufacturing. Find presentation slides here: https://ai.it.jyu.fi/ISM-2024-CONTEXT.pptx