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This chapter delves into the integration of symbolic knowledge into neural network architectures. We explore methodologies that treat symbolic rules as constraints during the learning process and those that embed this knowledge directly into the model’s structure. Through a detailed examination of techniques like regularization and model-based strategies, we provide insights into crafting AI systems that harmoniously blend data-driven adaptability with rule-based constraints, ensuring optimal performance in complex scenarios.

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Learning from Symbolic Knowledge for Neural Networks

  • 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 delves into the integration of symbolic knowledge into neural network architectures. We explore methodologies that treat symbolic rules as constraints during the learning process and those that embed this knowledge directly into the model’s structure. Through a detailed examination of techniques like regularization and model-based strategies, we provide insights into crafting AI systems that harmoniously blend data-driven adaptability with rule-based constraints, ensuring optimal performance in complex scenarios.