Artificial Intelligence: Theory and Practice

Author: Dean
Publisher:
Publish Date: 2004-06-01
Features: This book emphasizes the importance of expression in core chapters on logic, search, and learning. Compared to most introductory textbooks, it introduces more formal discussions of artificial intelligence, reflected in its focus on syntax and semantic logic, as well as in the coverage of computational complexity of AI algorithms. New results in computational learning theory will be included as supplementary material to illustrate various techniques ranging from decision trees to neural networks. The book provides examples of robots and software automation (software robots) as teaching cases in real-world AI, along with a wide range of other examples to highlight the potential and diversity of AI applications. Chapters on natural language processing, planning, uncertainty, and vision integrate existing methods, summarize challenging areas, and describe the prospects for technological advancements in further research. The book does not aim to cover all AI technologies; it briefly mentions topics such as qualitative reasoning and analogical reasoning in physical systems, while giving more attention to other subjects than traditional textbooks. It delves into learning, planning, and probabilistic reasoning to reflect their increased importance in the field. The chapter on vision (Chapter 9) covers topics crucial for understanding perception in intelligent systems and the development of artificial systems that interact with the world in practical and engaging ways. This is a textbook meticulously written by three senior AI experts, explaining the fundamental theories and practical applications of artificial intelligence. It introduces new research findings and discusses practical issues involved in system implementation, addressing expression and computational problems that arise in the development of machine intelligence systems. The authors explore traditional symbolic reasoning techniques for solving learning, planning, and uncertainty problems, such as deductive reasoning and decision trees, while also introducing new technologies like neural networks and probabilistic reasoning. Important algorithms in the book are accompanied by LISP source code implementations at the end of each chapter for reference during experimentation. Additionally, the book provides rich examples of AI application systems. It can serve as a textbook for AI courses in computer science, control engineering, mechatronics, mathematics, and other related fields at universities and colleges, as well as a reference for researchers and engineering professionals engaged in AI research and applications.