NEURO-SYMBOLIC INTEGRATION IN AI AGENTS: BRIDGING THE GAP BETWEEN PERCEPTION AND REASONING
Keywords:
Artificial Intelligence, Machine Learning, Neural Networks, Symbolic Reasoning, System IntegrationAbstract
This article explores the integration of neural networks and symbolic reasoning in artificial intelligence systems, presenting a comprehensive analysis of neuro-symbolic approaches. The article examines how this integration bridges the gap between connectionist and symbolic paradigms, enhancing both perception and reasoning capabilities. The article explores the foundational components, hierarchical architecture, and real-world applications of neuro-symbolic systems, demonstrating significant improvements in areas such as scientific discovery, natural language understanding, and robotics. The work highlights how these hybrid systems achieve enhanced generalization, improved interpretability, and increased robustness compared to traditional approaches. Through detailed analysis of both bottom-up and top-down processing streams, the article illustrates how neuro-symbolic integration addresses fundamental challenges in AI while maintaining computational tractability.
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Copyright (c) 2025 Bhabani Sankar Nayak (Author)
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