ANSR-DT: An Adaptive Neuro-Symbolic Learning and Reasoning Framework for Digital Twins

TL;DR

The paper addresses the interpretability and real-time adaptability gap in industrial digital twins by introducing ANSR-DT, a three-layer framework that integrates CNN-LSTM-based dynamic event detection, Prolog-based symbolic reasoning, and PPO reinforcement learning. It demonstrates that continuous rule updating and adaptive exploration yield high peak accuracy (99.5%) and improved explained variance (0.547) on synthetic industrial data, with 14 stable, human-interpretable rules. The approach strengthens trust in autonomous decisions by producing transparent reasoning chains and knowledge graphs, suitable for human-machine collaboration. While promising, scalability, computational overhead, and robustness to noise remain for future work.

Abstract

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.

Figures (8)

• Figure 1: Overview of the ANSR-DT framework architecture. The framework consists of three main layers: (1) Physical Industrial Environment for sensor integration and human operator interaction, (2) Processing Layer implementing the neuro-symbolic reasoning engine with deep learning and symbolic components, and (3) Adaptation Layer incorporating reinforcement learning and dynamic rule updating mechanisms. Solid arrows indicate primary data flow, while dashed arrows represent feedback loops and rule updates. The framework enables real-time human-machine collaboration through continuous adaptation and interpretable decision-making.
• Figure 2: System operation sequence of the ANSR-DT framework. The diagram illustrates the interaction between components such as the Sensor Network, Data Manager, ML Module, Rule Engine, Digital Twin, and User Interface for real-time time-series pattern extraction and adaptive operations.
• Figure 3: Multi-faceted visualization of synthetic sensor data: (a) Vibration measurements (mm/s) over operational hours showing operational thresholds, gradual events, and other key events; (b) Distribution of vibration sensor values distinguishing between normal operational data and key events; (c) Pressure readings (kPa) illustrating dynamic changes in system state during specific time steps. These visualizations demonstrate the realistic patterns and event characteristics incorporated in the synthetic dataset.
• Figure 4: ANSR-DT framework architecture with three layers: Physical Environment for sensor integration, Processing Layer for neuro-symbolic reasoning, and Adaptation Layer for reinforcement learning and rule updating. Solid arrows denote data flow, dashed arrows indicate feedback loops for dynamic adaptation.
• Figure 5: Training and validation accuracy and loss trends over 20 epochs, showing convergence with some fluctuations. Validation accuracy reached a peak of 99.5% by epoch 15, with class weighting (0: 64.7, 1: 0.5) addressing imbalance in the dataset.