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

TL;DR

ANSR-DT is a novel adaptive neuro-symbolic framework that enhances digital twin technology by integrating deep learning, reinforcement learning, and symbolic reasoning for improved interpretability and real-time decision-making in industrial environments.

Contribution

It introduces a hybrid neuro-symbolic approach combining CNN-LSTM, reinforcement learning, and symbolic reasoning to improve adaptability and interpretability of digital twins.

Findings

Achieved up to 99.5% accuracy in dynamic pattern recognition.

Improved explained variance from 0.447 to 0.547 with reinforcement learning.

Demonstrated superior adaptability over traditional methods.

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.