Neuro-Symbolic Artificial Intelligence: Concepts and Applications

Master the next frontier of artificial intelligence with this essential guide to uniting the pattern recognition of deep learning with the transparent, logical reasoning of symbolic AI.

The field of artificial intelligence has witnessed rapid advancements in recent years, driven primarily by deep learning and data-centric approaches. Despite their impressive performance, purely neural methods often lack interpretability, logical reasoning capabilities, and the ability to generalize beyond training data. In contrast, symbolic AI, rooted in formal logic and structured representations, offers transparency and reasoning strength, but struggles with adaptability and learning from raw data. In response to these challenges, neuro-symbolic AI has emerged as a compelling paradigm that unifies the strengths of both approaches. This book is a comprehensive exploration of one of the most transformative frontiers in artificial intelligence. By combining the pattern recognition power of neural networks with the logical reasoning capabilities of symbolic systems, neuro-symbolic AI promises to deliver systems that are not only accurate but also interpretable, adaptable, and aligned with human cognitive processes.

This book brings together a diverse range of research contributions that showcase both foundational theory and practical applications across domains like natural language processing, healthcare, intelligent transport, cybersecurity, and ethical AI. Spanning topics such as hybrid architectures, logic-enhanced deep learning, graph neural networks, transfer learning, and explainable AI, the volume addresses the technical and conceptual challenges of building trustworthy intelligent systems. Each chapter provides technical depth, experimental insights, and future directions, making this guide a vital resource for researchers, graduate students, and professionals in AI and machine learning.

Readers will find the volume introduces the fundamental concepts of neuro-symbolic AI, explores real-world applications in healthcare, natural language processing, and ethical AI, and presents a forward-looking perspective on the next generation of robust, transparent, and trustworthy AI technologies.

Audience

Engineering research scholars and students, IT professionals, network administrators, artificial intelligence and deep learning experts, and government research agencies.

R. Nidhya, PhD is a Professor in the Department of Computer Science and Engineering, Manipal Institute of Technology and Science, Madanapalle, India, with more than 16 years of teaching experience. She has published many research papers in refereed international journals and conferences. Her research interests include machine learning, wireless body area networks, and network security.

A. Dineshkumar, PhD is an Associate Professor at Koneru Lakshmaiah Education Foundation, Vijayawada, Andhra Pradesh, India. He completed his PhD at Anna University in Chennai in 2018. His current research interests include wireless body area networks, wireless sensor networks, network security, and artificial intelligence.

Sheng-Lung Peng, PhD is a Professor and the Director of the Department of Creative Technologies and Product Design, National Taipei University of Business, Taiwan. He has edited several special issues of journals and published more than 100 research articles. His research interests are in designing and analyzing algorithms for bioinformatics, combinatorics, data mining, and networks.

S. Karthik, PhD is a Professor and Dean in the Department of Computer Science and Engineering, SNS College of Technology, Anna University, Chennai, Tamil Nadu, India. He has published more than 150 papers in refereed international journals and 125 papers in international conferences. His research interests include network security, big data, cloud computing, web services, and wireless systems.

S. Balamurugan, PhD is the Director of Research, Intelligent Research Consultancy Services, Coimbatore, Tamil Nadu, India. He has published 100 books, 300 papers in international journals and conferences, and 300 patents. With 20 years of research on various cutting-edge technologies, he provides expert guidance in technology forecasting and decision-making for leading companies and startups.