Open Access

NEUROSYMBOLIC AI: MERGING DEEP LEARNING AND LOGICAL REASONING FOR ENHANCED EXPLAINABILITY

https://doi.org/10.55640/10.55640/ijaair-v02i06-01

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Sara Rossi ¹ , Samuel Johnson ¹ ,

⁴ Institute for Data Science, ETH Zürich, Switzerland

⁴ Department of Electrical Engineering and Computer Sciences, UC Berkeley, USA

Abstract

Neurosymbolic Artificial Intelligence (AI) represents a promising paradigm that bridges the gap between sub-symbolic learning and symbolic reasoning by integrating deep learning models with formal logic-based systems. This hybrid approach leverages the pattern recognition strengths of neural networks and the interpretability and generalization power of symbolic reasoning. The convergence of these two methodologies addresses key challenges in AI, such as explainability, data efficiency, and reasoning under uncertainty. This paper explores the conceptual foundations, architectures, and recent advancements in neurosymbolic systems, highlighting their applications in domains requiring high levels of transparency and human-aligned reasoning, such as healthcare, legal systems, and scientific discovery. Furthermore, the study discusses open research questions and future directions aimed at developing scalable, robust, and interpretable AI systems.

Keywords

Neurosymbolic AI, Deep Learning, Symbolic Reasoning, Explainable AI

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International Journal of Advanced Artificial Intelligence Research

NEUROSYMBOLIC AI: MERGING DEEP LEARNING AND LOGICAL REASONING FOR ENHANCED EXPLAINABILITY

Abstract

Keywords

References

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