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

2025-01-15

AI Summary

To address low decision accuracy, poor reliability, limited interpretability, and inadequate human–machine collaboration in digital twin systems operating within complex, dynamic, and heterogeneous environments, this paper proposes a neuro-symbolic adaptive coupling framework. The framework integrates deep neural networks, reinforcement learning, and a symbolic rule engine, unifying environmental perception, continual online learning, and explicit logical reasoning through real-time knowledge distillation and dynamic rule injection. Its novel coupling mechanism ensures traceable decision-making, updatable symbolic rules, and verifiable model behavior. Experimental evaluation across multiple digital twin scenarios demonstrates a 19.3% improvement in decision accuracy, a 37% reduction in fault response latency, and an interpretability score 2.1× higher than state-of-the-art methods. These results significantly advance the deployment of trustworthy, human–machine collaborative decision-making in digital twin applications.

Technology Category

• Machine Learning: Neuro-Symbolic Learning
• Cognitive Modeling & Cognitive Systems: Symbolic Representations
• Humans and AI: Planning and Decision Support for Human-Machine Teams

Application Category

• Semantics and Knowledge: Data modeling to support human-machine intelligence, including LLMs agents, intelligent system behavior, explanations, and user-friendly interactions
• Systems and Infrastructure for Web, Mobile and WoT: Applied ML and AI for Web-based mobile applications
• Responsible Web: Machine-in-the-loop, human agency and autonomy

Abstract

In this paper, we propose an Adaptive Neuro-Symbolic Learning Framework for digital twin technology called ``ANSR-DT."Our approach combines pattern recognition algorithms with reinforcement learning and symbolic reasoning to enable real-time learning and adaptive intelligence. This integration enhances the understanding of the environment and promotes continuous learning, leading to better and more effective decision-making in real-time for applications that require human-machine collaboration. We evaluated the extit{ANSR-DT} framework for its ability to learn and adapt to dynamic patterns, observing significant improvements in decision accuracy, reliability, and interpretability when compared to existing state-of-the-art methods. However, challenges still exist in extracting and integrating symbolic rules in complex environments, which limits the full potential of our framework in heterogeneous settings. Moreover, our ongoing research aims to address this issue in the future by ensuring seamless integration of neural models at large. In addition, our open-source implementation promotes reproducibility and encourages future research to build on our foundational work.

Problem

• Digital Twin
• Decision Accuracy
• Heterogeneous Environment

Innovation

• Adaptive Neuro-Symbolic Learning
• Digital Twin Technology
• Human-Machine Collaboration