Vol. 1, Issue 1, Part A (2024)

The study explores a unified framework designed to merge the interpretability of symbolic artificial intelligence with the representational power of deep neural networks. Traditional deep learning models, while achieving exceptional accuracy in perception-based tasks, often lack transparency and logical consistency, leading to challenges in explainability and reasoning-based decision-making. Conversely, purely symbolic systems struggle to scale and adapt to unstructured data environments. This research bridges these limitations by developing and evaluating hybrid architectures that integrate symbolic reasoning modules with deep learning frameworks through differentiable logic constraints. Experimental evaluations across reasoning-intensive datasets such as CLEVR and bAbI demonstrated significant improvements in accuracy, consistency, and explainability compared to conventional deep models. Statistical analyses confirmed that the hybrid model achieved higher reasoning accuracy with reduced rule-violation rates, validating the hypothesis that embedding symbolic structure into neural learning enhances both performance and interpretability. Moreover, explainability assessments revealed improved alignment between model reasoning and human-understandable logic, thereby increasing trustworthiness in high-stakes applications. The study concludes that hybrid intelligence represents a viable path toward achieving general, interpretable, and ethically aligned AI systems capable of both learning from data and reasoning through knowledge. Practical implications are evident in fields such as healthcare, finance, law, and autonomous systems, where accountability and transparent decision-making are critical. The integration of symbolic reasoning with deep learning thus lays a foundational framework for developing next-generation AI systems that are not only accurate but also explainable, reliable, and aligned with human cognitive principles.