Knowledge-Infused Learning : Neurosymbolic AI for Explainability, Interpretability, and Safety

Gaur, Manas/ Sheth, Amit P.

Cambridge University Press（2025/10発売）

• 製本 Hardcover:ハードカバー版／ページ数 310 p.
• 言語 ENG
• 商品コード 9781009513746

Full Description

Knowledge-infused learning directly confronts the opacity of current 'black-box' AI models by combining data-driven machine learning techniques with the structured insights of symbolic AI. This guidebook introduces the pioneering techniques of neurosymbolic AI, which blends statistical models with symbolic knowledge to make AI safer and user-explainable. This is critical in high-stakes AI applications in healthcare, law, finance, and crisis management. The book brings readers up to speed on advancements in statistical AI, including transformer models such as BERT and GPT, and provides a comprehensive overview of weakly supervised, distantly supervised, and unsupervised learning methods alongside their knowledge-enhanced variants. Other topics include active learning, zero-shot learning, and model fusion. Beyond theory, the book presents practical considerations and applications of neurosymbolic AI in conversational systems, mental health, crisis management systems, and social and behavioral sciences, making it a pragmatic reference for AI system designers in academia and industry.

Contents

1. Introduction; 2. Knowledge graphs for explainability and interpretability; 3. Knowledge-infused learning: the subsumer to neurosymbolic AI; 4. Shallow infusion of knowledge; 5. Semi-deep infusion learning; 6. Deep knowledge-infused learning; 7. Process knowledge-infused learning; 8. Knowledge-infused conversational NLP; 9. Neurosymbolic large language models; References; Index.