Neuro-Symbolic Programming with Dynamic Rule Extraction

Abstract

This paper proposes a novel approach to artificial intelligence combining the strengths of neural networks and symbolic reasoning through dynamic rule extraction. The core idea is to leverage neural networks for pattern recognition and expression generation, followed by a dynamic rule extraction module that iteratively refines these expressions into logical rules. This process ensures that the extracted rules are both consistent and supported by the underlying data. Unlike traditional rule-based systems, this method offers enhanced explainability and robustness by allowing the system to learn and refine its reasoning capabilities directly from data. The system's architecture involves a neural network component that maps input data to symbolic expressions, and a rule extraction module which employs a consistency checking mechanism and iterative refinement steps. The key innovation lies in the dynamic adaptation of the rules based on continuous feedback, resulting in a more adaptable and reliable AI system. This approach addresses limitations of static rule systems and simple neural networks by providing a framework for intelligent rule discovery.