Neural Symbolic Hybrid Systems for Robust Reasoning

Abstract

This paper proposes a novel approach to artificial intelligence by integrating neural networks and symbolic reasoning, resulting in Neural Symbolic Hybrid Systems. The core claim is that combining the strengths of both paradigms—the pattern recognition capabilities of neural networks and the logical inference capabilities of symbolic systems—yields more robust and explainable AI systems. The proposed system utilizes neural networks for perceptual tasks, such as feature extraction and initial data interpretation, while employing symbolic reasoning engines for higher-level logical inference, decision-making, and knowledge representation. The system architecture is designed to facilitate seamless interaction between these two components. Specifically, the neural network outputs are translated into symbolic representations, which are then processed by the reasoning engine. Conversely, the symbolic reasoning engine's conclusions are fed back into the neural network for refinement and improved accuracy. This hybrid approach addresses limitations inherent in purely neural or purely symbolic systems. Neural networks alone can lack explainability and robustness against noisy or incomplete data, while symbolic systems often struggle with complex, real-world scenarios requiring flexible pattern recognition. This paper outlines the core mechanisms, architecture, and potential benefits of such a system, highlighting its capacity for robust reasoning and enhanced interpretability. The system is envisioned as a significant step towards developing more reliable and trustworthy AI solutions.