Neuro-Symbolic Synergy for Deep Adaptive Intelligence: A Hierarchical Framework for Explainability and Reasoning

Abstract

In recent years, the convergence of deep learning and artificial intelligence (AI) has reshaped the landscape of computational intelligence and intelligent automation. Deep learning provides the representational capacity necessary to extract complex features from vast data streams, while AI offers reasoning and decision-making frameworks that enable adaptive and explainable systems. This paper proposes a synergistic framework that unifies deep neural architectures with AI reasoning modules to create adaptive, scalable, and interpretable intelligent systems. The proposed architecture integrates hierarchical feature extraction, dynamic knowledge representation, and reinforcement-driven adaptation mechanisms to enhance both perception and cognition. A comparative analysis with state-of-the-art methods demonstrates the potential of deep-AI synergy in improving generalization, transparency, and computational efficiency. The findings suggest that the fusion of deep learning and AI not only improves domain-specific performance but also moves one step closer to general-purpose intelligence capable of self-adaptation and human-like reasoning.