Neuro-Symbolic AI: Bridging Deep Learning and Symbolic Reasoning for Cognitive Computation

Abstract : The rapid progress of deep learning has yielded powerful models capable of pattern recognition, perception, and language generation. However, these models often lack the capacity for logical reasoning, abstraction, and generalization—capabilities long associated with symbolic AI. To achieve truly human-like intelligence and robust cognitive computation, researchers are increasingly focusing on Neuro-Symbolic AI (NSAI)—an integrative approach that merges neural learning with symbolic reasoning. This paper presents a comprehensive framework for Neuro-Symbolic AI that leverages the strengths of both paradigms to address complex tasks requiring both perceptual and logical capabilities. The proposed system combines neural networks for data-driven perception with symbolic modules for rule-based reasoning, enabling interpretability, generalizability, and data efficiency. We explore applications across knowledge graph reasoning, visual question answering, and cognitive modeling, demonstrating how NSAI systems outperform purely neural or purely symbolic methods in terms of robustness, explainability, and adaptability. We also analyze state-of-the-art architectures such as Logic Tensor Networks (LTNs), Neural Theorem Provers (NTPs), and Differentiable Inductive Logic Programming (∂ILP), highlighting how they model symbolic knowledge within neural frameworks. Experimental results validate the effectiveness of NSAI in achieving explainable predictions and few-shot generalization across diverse domains. Finally, we discuss the philosophical and practical implications of Neuro-Symbolic AI in replicating human cognitive processes, paving the way for next-generation intelligent systems that blend learning and reasoning. This hybrid approach represents a foundational step toward achieving Artificial General Intelligence (AGI) with real-world applications in education, robotics, healthcare, and autonomous systems.

Keyword Neuro-Symbolic AI, Deep Learning, Symbolic Reasoning, Cognitive Computation, Artificial General Intelligence, Logic Tensor Networks, Neural Theorem Proving, Explainable AI, Knowledge Representation, Hybrid Intelligence Systems.