ANSR-DT: A Neuro-Symbolic Framework for Adaptive and Explainable Digital Twins

ANSR-DT is a newly introduced neuro-symbolic framework designed for adaptive and explainable digital twins, integrating temporal anomaly detection, symbolic reasoning, and reinforcement learning. It employs a CNN-LSTM architecture for multivariate pattern recognition and Prolog for rule extraction, enhancing interpretability and decision-making transparency. Benchmark tests against eight models demonstrate its competitive predictive performance and effective adaptation through a PPO-based layer, making it a significant advancement for practitioners seeking reliable and explainable solutions in industrial applications.