Artificial Intelligence: Principles and Practice

This book offers a comprehensive introduction to Artificial Intelligence, blending foundational computational technologies with essential mathematical principles and philosophical considerations. Titled *Artificial Intelligence: Principles and Practice*, the text emphasizes the field's interdisciplinary nature by integrating insights from psychology, neuroscience, and engineering. It is designed with a modular structure that allows instructors and students to focus on specific components while maintaining a holistic view of the discipline. The content spans from historical backgrounds to modern design practices, making it suitable for both undergraduate and graduate-level courses. The core of the book details the three primary paradigms of current AI practice: symbol-based approaches, neural networks or connectionist models, and probabilistic methods. These technical sections are framed by Part I, which establishes the philosophical and engineering basis for AI, and Part VIII, which addresses critical ethical concerns and fundamental limitations. Complex algorithms and processes are explained clearly, supported by numerous examples and end-of-chapter exercises to reinforce learning. This approach ensures readers can master the necessary methods for creating intelligent artifacts. Beyond technical instruction, the text places significant weight on the ethical implications of integrating AI into modern society. It discusses the future promise of the technology alongside the limiting factors that developers must navigate. To support the educational experience, the book includes teaching resources such as a solutions manual, PowerPoint presentations, and algorithm implementations. By combining rigorous technical training with ethical analysis, this volume provides a complete foundation for understanding the evolving landscape of artificial intelligence.