Neurosymbolic AI: Integrating Logical Reasoning with Deep Learning for Complex Problem Solving

Abstract

Neurosymbolic AI is an emerging paradigm that seeks to bridge the gap between deep learning
(sub-symbolic AI) and symbolic reasoning-based AI. Traditional deep learning models excel in
pattern recognition, perception, and high-dimensional data processing, whereas symbolic AI is
strong in explicit reasoning, logical inference, and knowledge representation. However, deep
learning often lacks interpretability, and symbolic AI struggles with scalability and learning from
raw data. Neurosymbolic AI combines these two approaches to create AI systems capable of
learning, reasoning, and making explainable decisions. This paper presents a comprehensive
analysis of state-of-the-art neurosymbolic architectures, methodologies, and applications in fields
such as natural language processing (NLP), mathematics, robotics, and scientific discovery. We
conduct experiments comparing deep learning, symbolic AI, and neurosymbolic AI on various
reasoning tasks. The results demonstrate superior performance, improved generalization, and
better interpretability of neurosymbolic AI over traditional AI paradigms. We also discuss key
challenges and future directions for enhancing neurosymbolic AI systems.