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RAISE: A Reinforcement Learning Framework for Adaptive, Context-Aware Explainable AI in Industrial Cyber-Physical Systems

 

 

Satyendra Kumar Shukla1  Mohd Umar Abdullah2  Altaf Ali 3

1Asst. Prof. Department of Mechanical Engineering, FoET, Khwaja Moinuddin Chishti Language,University, Lucknow.

23Student of Mechanical Engineering, FoET, Khwaja Moinuddin Chishti Language University, Lucknow.

 

 

ABSTRACT

The vision of Industry 4.0 is predicated on the seamless integration of advanced Artificial Intelligence and Machine Learning (AI/ML) models into the very fabric of industrial operations, enabling autonomous, self-optimizing, and resilient Cyber-Physical Systems (CPS). From predictive maintenance to real-time quality control and robotic process automation, the adoption of these complex models promises unprecedented efficiency and capability. However, this transformative potential is fundamentally constrained by the pervasive opacity of high-performance "black-box" models, such as deep neural networks and sophisticated ensembles. This opacity erodes human trust, complicates regulatory compliance, and presents a significant barrier to effective human-machine collaboration in safety-critical and economically consequential environments. While the field of Explainable AI (XAI) has emerged to provide post-hoc transparency through methods like SHAP and LIME, these techniques are inherently limited. They are often computationally prohibitive for real-time industrial applications, generate static and uniform explanations irrespective of context or user, and fail to provide the nuanced, actionable insights required for industrial decision-making. This paper introduces RAISE (Reinforcement-Augmented Interpretable Structured Explanations), a novel and comprehensive XAI framework designed to overcome these limitations. RAISE reconceptualizes explanation generation as a dynamic, context-sensitive sequential decision-making problem. At its core, a lightweight Proximal Policy Optimization (PPO) agent, trained on a multi-fidelity reward function, dynamically selects the optimal explanation strategy from a diverse portfolio—including contrastive, causal, feature-importance, and counterfactual methods—tailored to the specific data instance, the underlying model's state, and the immediate operational context. This adaptive mechanism ensures that the explanation provided is not only faithful to the original model but also maximally interpretable and useful for the human stakeholder, whether they are a machine operator, a maintenance engineer, or a system designer. We present a complete formalization of the problem as a Markov Decision Process (MDP), detailing the architecture and training protocol. A rigorous experimental evaluation on established industrial datasets demonstrates that RAISE achieves a statistically significant 22.7% improvement in human-rated interpretability scores over state-of-the-art static baselines while maintaining 98.3% fidelity. Furthermore, RAISE reduces average explanation latency by 34.1%, proving its viability for real-time, edge-based deployment. By providing a pathway toward trustworthy, efficient, and human-centric XAI, the RAISE framework directly addresses critical research gaps in Industry 4.0, particularly in the domains of scalable human-AI teaming and the responsible deployment of AI in complex, dynamic industrial ecosystems.

INDEX TERMS: Explainable Artificial Intelligence (XAI), Reinforcement Learning, Industry 4.0, Human-in-the-Loop AI, Adaptive Systems, Cyber-Physical Systems, Trustworthy AI, Predictive Maintenance, Context-Aware Computing.

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