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This introductory chapter presents neuro-symbolic AI as a unified framework that combines deductive reasoning with inductive deep learning to enable transparent, adaptive decision-making. It shows how hybrid systems address the limitations of purely symbolic or statistical methods through structured reasoning and interpretable generalization. The chapter highlights discourse analysis as a key mechanism for aligning LLM outputs with human reasoning and communicative intent. Examples from finance, law, health, and engineering demonstrate how explainability, personalization, and domain knowledge benefit from neuro-symbolic integration. The chapter concludes by underscoring the need for human-aligned, ethically grounded AI in complex domains.

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Introduction

  • 章节

  • 发表日期：

    2026年

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  • Boris Galitsky

摘要

This introductory chapter presents neuro-symbolic AI as a unified framework that combines deductive reasoning with inductive deep learning to enable transparent, adaptive decision-making. It shows how hybrid systems address the limitations of purely symbolic or statistical methods through structured reasoning and interpretable generalization. The chapter highlights discourse analysis as a key mechanism for aligning LLM outputs with human reasoning and communicative intent. Examples from finance, law, health, and engineering demonstrate how explainability, personalization, and domain knowledge benefit from neuro-symbolic integration. The chapter concludes by underscoring the need for human-aligned, ethically grounded AI in complex domains.