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This chapter explores the integration of symbolic reasoning and machine learning within the field of Neuro-Symbolic AI, a significant advancement in artificial intelligence. We investigate various reasoning methods, including deductive, inductive, abductive, analogical, probabilistic, common sense, and combinatorial reasoning. This foundation aids in understanding the cognitive processes fundamental to Neuro-Symbolic AI. Additionally, we delve into the concepts of System 1 and System 2 cognition, connecting neural networks and symbolic AI systems. The chapter highlights the fusion of Neuro-Symbolic AI, combining the strengths of both neurocomputational and symbolic systems to create AI systems that are both efficient and intuitive, capable of learning from data and reasoning logically. This discussion sets the stage for further exploration in subsequent chapters.

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Neuro-Symbolic AI: The Fusion of Symbolic Reasoning and Machine Learning

  • 章节

  • 发表日期：

    2025年

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  • Bikram Pratim Bhuyan,
  • Amar Ramdane-Cherif,
  • Thipendra P. Singh,
  • Ravi Tomar

摘要

This chapter explores the integration of symbolic reasoning and machine learning within the field of Neuro-Symbolic AI, a significant advancement in artificial intelligence. We investigate various reasoning methods, including deductive, inductive, abductive, analogical, probabilistic, common sense, and combinatorial reasoning. This foundation aids in understanding the cognitive processes fundamental to Neuro-Symbolic AI. Additionally, we delve into the concepts of System 1 and System 2 cognition, connecting neural networks and symbolic AI systems. The chapter highlights the fusion of Neuro-Symbolic AI, combining the strengths of both neurocomputational and symbolic systems to create AI systems that are both efficient and intuitive, capable of learning from data and reasoning logically. This discussion sets the stage for further exploration in subsequent chapters.