Journal of Artificial Intelligence, Machine Learning and Soft Computing

Abstract

Neurosymbolic Artificial Intelligence (AI) represents a promising paradigm that bridges the gap between neural networks and symbolic reasoning. Traditional neural methods excel at pattern recognition from large datasets but often lack explainability and reasoning capabilities. Conversely, symbolic AI offers robust logical reasoning but struggles with unstructured data and learning from examples. Neurosymbolic AI integrates these paradigms, combining neural models' adaptability with symbolic systems' interpretability and knowledge representation. This paper reviews the state-of-the-art in neurosymbolic AI, covering its conceptual framework, modeling techniques, applications across domains, challenges, and future directions. The integration of neural and symbolic methods offers a path toward more robust, interpretable, and generalizable AI systems, with potential implications for healthcare, robotics, natural language understanding, and autonomous systems.

Keywords: Neurosymbolic AI, Neural Networks, Symbolic Reasoning, Knowledge Representation, Explainable AI, Hybrid Intelligence