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.
