Knowledge-Infused Learning

Manas Gaur

ISBN 10: 1009513745 / ISBN 13: 9781009513746

Published by Cambridge University Press, 2026

About this Item

Druck auf Anfrage Neuware - Printed after ordering - 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.

Seller Inventory # 9781009513746

Bibliographic Details

• Title: Knowledge-Infused Learning
• Publisher: Cambridge University Press
• Publication Date: 2026
• Language: English
• ISBN 10: 1009513745
• ISBN 13: 9781009513746
• Binding: Buch
• Condition: Neu
• Dimensions: 235W x 157H x 22D millimeters 235 millimeters width by 157 millimeters height by 22 millimeters depth
• Weight: 622 grams

About this title

Synopsis

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.

About the Authors

Manas Gaur is an assistant professor in the Department of Computer Science and Electrical Engineering at the University of Maryland, Baltimore County (UMBC). He earned his Ph.D. in 2022 from the University of South Carolina's Artificial Intelligence Institute, studying under Dr. Amit P. Sheth. A pioneer in knowledge-infused learning (2016–2022), Gaur's research has earned multiple best paper awards and recognition through USC Eminent Profiles and AAAI New Faculty Highlights. His cutting-edge work continues to attract major funding, including grants from NSF and EPSRC-UKRI in partnership with the Alan Turing Institute.

Amit P. Sheth is the NCR Chair and Professor of Computer Science and Engineering at the University of South Carolina, where he founded the university-wide AI Institute in 2019 and grew it to nearly 50 AI researchers in four years. He is a fellow of IEEE, AAAI, AAAS, ACM, and AIAA. His awards include the IEEE CS Wallace McDowell Award and the IEEE TCSVC Research Innovation Award. He has co-founded four companies, run two of them, and advised or mentored over 45 Ph.D. candidates and postdocs to exceptional careers in academia, industry, and as entrepreneurs.