Neuro-Symbolic AI: Integrating Deep Learning with Symbolic Logic for Enhanced Reasoning

https://doi.org/10.5281/zenodo.19121134

Abstract

The evolution of artificial intelligence in 2026 has necessitated the integration of deep learning's perceptual strengths with symbolic logic's interpretive rigor, giving rise to Neuro-Symbolic AI (NSAI). This review explores NSAI's foundations, drawing from the "two cultures" of AI symbolic rationalism and connectionist empiricism to address the opacity, inefficiency, and regulatory challenges of standalone neural models. Key architectures, such as Neural Probabilistic Logic Programming (NeurASP), DeepStochLog, and Logic Tensor Networks (LTNs), are examined for their ability to embed symbolic rules into neural frameworks, enabling tasks like visual grounding, commonsense reasoning, and ethical decision-making in high-stakes domains (e.g., healthcare, autonomous systems). Case studies highlight NSAI's superiority in hybrid reasoning, with applications in predictive maintenance, process control, and structural engineering demonstrating 20–50% improvements in accuracy and explainability. Challenges including computational scalability and knowledge extraction are discussed, alongside future directions emphasizing differentiable logic and multi-modal integration for trustworthy AI.