Neural Symbolic Reasoning Engine

Abstract

This paper proposes a novel Neural Symbolic Reasoning Engine (NSRE) designed to bridge the gap between the pattern recognition capabilities of neural networks and the logical reasoning capabilities of symbolic systems. The core claim is that combining these two approaches yields a system with enhanced knowledge representation and reasoning abilities. The mechanism involves constructing an NSRE that leverages neural networks for knowledge extraction and utilizes symbolic reasoning algorithms for logical inference and knowledge transfer. The system addresses the limitations of current approaches in representing and reasoning with complex knowledge. The architecture consists of a neural network component, responsible for encoding contextual information and identifying relevant knowledge fragments, and a symbolic reasoning engine, which employs a rule-based system to perform logical deductions and integrate the extracted knowledge. This approach offers a pathway to more robust and explainable AI systems, particularly in domains requiring both perceptual understanding and logical deduction. We explore the fundamental components and the interaction between the neural and symbolic modules, outlining a framework for building intelligent systems capable of dynamic knowledge acquisition and sophisticated reasoning. The goal is to create a system that can not only recognize patterns but also reason about them in a logically consistent manner, ultimately leading to more reliable and adaptable AI.