Neuro-Symbolic AI: Combining Learning and Reasoning
DOI:
https://doi.org/10.63282/3117-5481/AIJCST-V5I4P105Keywords:
Neuro-Symbolic AI, Neural Networks, Symbolic Reasoning, Knowledge Representation, Explainable AI, Logic-Based AI, Learning and Reasoning, Hybrid AI Systems, Cognitive AI, Knowledge Graphs, GeneralizationAbstract
The field of artificial intelligence has long been divided between two paradigms: data-driven, connectionist approaches such as deep learning, and knowledge-driven, symbolic reasoning systems. While deep learning has achieved remarkable success in perception tasks like image recognition, natural language understanding, and speech processing, it often struggles with abstract reasoning, logical inference, and systematic generalization. Symbolic AI, in contrast, excels at explicit reasoning, knowledge representation, and interpretability but lacks the ability to learn effectively from unstructured, high-dimensional data. Neuro-symbolic AI represents an emerging paradigm that seeks to integrate the strengths of both approaches, combining the pattern recognition capabilities of neural networks with the logical reasoning power of symbolic systems. By unifying learning and reasoning, neuro-symbolic AI aims to create AI systems capable of robust decision-making, explainable inference, and generalization across diverse domains. This article presents a comprehensive exploration of neuro-symbolic AI, covering its theoretical foundations, architectural frameworks, learning methodologies, applications in natural language understanding, robotics, and knowledge-based reasoning, as well as challenges, ethical considerations, and future research directions. The fusion of symbolic reasoning with neural computation promises to advance the development of general-purpose intelligent systems that are both data-efficient and capable of human-like reasoning.
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