Bridging the Gap: A Literature Review of Neuro-Symbolic Approaches in Artificial Intelligence

This literature review provides a comprehensive analysis of Neuro-Symbolic Artificial Intelligence (NeSy), a rapidly advancing field that integrates data-driven deep learning with knowledgebased symbolic reasoning. This paper begin by tracing the historical and philosophical schism between the connectionist and symbolic paradigms, establishing the context for the current "third wave" of AI, where their synthesis has become a central research imperative. The review then presents a systematic categorization of NeSy architectures, with a detailed examination of Kautz’s influential taxonomy, contrasting it with functional classifications to provide a multi-faceted understanding of the field. This paper conduct an in-depth analysis of seminal models and frameworks—from AlphaGo and the Neuro-Symbolic Concept Learner to Logic Tensor Networks and modern Large Language Models augmented with symbolic tools—elucidating the diverse strategies for bridging the neural-symbolic gap. Subsequently, this paper survey key application domains where NeSy is demonstrating significant impact, including explainable AI, natural language understanding, visual reasoning, and robotics. Finally, this paper critically assess the field’s primary challenges, including symbol grounding, scalability, and the need for robust evaluation benchmarks, and conclude by outlining the future research frontiers of causal inference, meta-cognition, and the pursuit of more general and trustworthy AI.