A Novel Neuro-Symbolic AI Approach for Bridging Deep Learning and Logical Reasoning
Abstract
Deep learning excels at pattern recognition, while symbolic AI offers superior logical reasoning capabilities. This paper introduces a novel neuro-symbolic AI framework that combines neural networks with rule-based logic systems to enhance decision-making in complex domains. We evaluate our approach against standalone deep learning and symbolic reasoning models in applications such as medical diagnostics, autonomous systems, and legal AI. Experimental results indicate that our framework improves interpretability, reasoning accuracy, and robustness, bridging the gap between perception and logic in AI.
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