ANSR-DT: An Adaptive Neuro-Symbolic Learning and Reasoning Framework for Digital Twins
This addresses interpretability and adaptation problems for industrial digital twin applications, though it appears incremental as it combines existing methods like CNN-LSTM, reinforcement learning, and symbolic reasoning.
The paper tackles the challenges of interpretability, real-time adaptation, and human input integration in digital twins for industrial environments by proposing ANSR-DT, a neuro-symbolic framework that achieved up to 99.5% accuracy for dynamic pattern recognition and improved explained variance from 0.447 to 0.547.
In this paper, we propose an Adaptive Neuro-Symbolic Learning and Reasoning Framework for digital twin technology called ``ANSR-DT." Digital twins in industrial environments often struggle with interpretability, real-time adaptation, and human input integration. Our approach addresses these challenges by combining CNN-LSTM dynamic event detection with reinforcement learning and symbolic reasoning to enable adaptive intelligence with interpretable decision processes. This integration enhances environmental understanding while promoting continuous learning, leading to more effective real-time decision-making in human-machine collaborative applications. We evaluated ANSR-DT on synthetic industrial data, observing significant improvements over traditional approaches, with up to 99.5% accuracy for dynamic pattern recognition. The framework demonstrated superior adaptability with extended reinforcement learning training, improving explained variance from 0.447 to 0.547. Future work aims at scaling to larger datasets to test rule management beyond the current 14 rules. Our open-source implementation promotes reproducibility and establishes a foundation for future research in adaptive, interpretable digital twins for industrial applications.