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人工智能：原理与实践
基本信息
George F. Luger, Artificial Intelligence: Principles and Practice, Cham: Springer Nature, 2025.
内容简介
This book provides a complete introduction to Artificial Intelligence, covering foundational computational technologies, mathematical principles, philosophical considerations, and engineering disciplines essential for understanding AI. Artificial Intelligence: Principles and Practice emphasizes the interdisciplinary nature of AI, integrating insights from psychology, mathematics, neuroscience, and more. The book addresses limitations, ethical issues, and the future promise of AI, emphasizing the importance of ethical considerations in integrating AI into modern society. With a modular design, it offers flexibility for instructors and students to focus on specific components of AI, while also providing a holistic view of the field.
Taking a comprehensive but concise perspective on the major elements of the field; from historical background to design practices, ethical issues and more, Artificial Intelligence: Principles and Practice provides the foundations needed for undergraduate or graduate-level courses. The important design paradigms and approaches to AI are explained in a clear, easy-to-understand manner so that readers will be able to master the algorithms, processes, and methods described.
The principal intellectual and ethical foundations for creating artificially intelligent artifacts are presented in Parts I and VIII. Part I offers the philosophical, mathematical, and engineering basis for our current AI practice. Part VIII presents ethical concerns for the development and use of AI. Part VIII also discusses fundamental limiting factors in the development of AI technology as well as hints at AI's promising future. We recommended that PART I be used to introduce the AI discipline and that Part VIII be discussed after the AI practice materials. Parts II through VII present the three main paradigms of current AI practice: the symbol-based, the neural network or connectionist, and the probabilistic.
Generous use of examples throughout helps illustrate the concepts, and separate end-of-chapter exercises are included. Teaching resources include a solutions manual for the exercises, PowerPoint presentation, and implementations for the algorithms in the book.
本书全面介绍了人工智能，涵盖了理解人工智能所必需的基础计算技术、数学原理、哲学思考和工程学科人工智能。《人工智能：原理与实践》强调了人工智能的跨学科性质，整合了心理学、数学、神经科学等领域的见解。本书探讨了人工智能的局限性、伦理问题和未来前景，强调在将人工智能融入现代社会时考虑伦理因素的重要性。通过模块化设计，本书为教师和学生提供了灵活性，既可以专注于人工智能的特定组成部分，同时又能提供该领域的整体视角。
本书对该领域的主要要素采取了全面而简明的视角，从历史背景到设计实践、伦理问题等，《人工智能：原理与实践》为本科或研究生层次的课程提供了必要的基础人工智能。书中以清晰、易懂的方式解释了人工智能的重要设计范式和方法，使读者能够掌握所描述的算法、过程和方法。
第一部分和第八部分介绍了创建人工智能产物的主要智识和伦理基础人工智能。第一部分提供了当前人工智能实践的哲学、数学和工程基础。第八部分阐述了人工智能发展和使用中的伦理问题。第八部分还讨论了人工智能技术发展的根本限制因素，并暗示了人工智能的光明前景。我们建议用第一部分介绍人工智能学科，在学习完人工智能实践材料后再讨论第八部分。第二至第七部分介绍了当前人工智能实践的三个主要范式：基于符号的、神经网络或连接主义的，以及概率的。
全书大量使用示例帮助阐述概念，并在每章末尾附有独立的练习人工智能。教学资源包括练习解答手册、PowerPoint演示文稿，以及书中算法的实现代码。
作者简介
George Luger is Professor Emeritus of Computer Science at the University of New Mexico. Dr. Luger was also a Professor in the Psychology and Linguistics Departments, reflecting his interdisciplinary interests in Cognitive Science and Computational Linguistics.
The National Science Foundation, NATO, the British Royal Society, NASA, the Smithsonian Institution, NIH, the Departments of Defense, Energy and Transportation, NIH, and other government agencies have supported George Luger's research. He has worked with the Los Alamos and Sandia National Laboratories and for numerous companies. Currently, his consulting is in the design of natural language web agents and deep learning technologies that analyze information in very large collections of data.
Dr. Luger is the author of Artificial Intelligence: Structures and Strategies for Complex Problem Solving (Addison-Wesley, 2009), now in its Sixth Edition, and Knowing Our World (Springer, 2021).
乔治·卢格尔是新墨西哥大学计算机科学系荣休教授人工智能。卢格尔博士同时也是心理学和语言学系的教授，这反映了他在认知科学和计算语言学方面的跨学科兴趣。美国国家科学基金会、北约、英国皇家学会、美国宇航局、史密森学会、美国国立卫生研究院、国防部、能源部和运输部等政府机构都支持过乔治·卢格尔的研究。他曾与洛斯阿拉莫斯和桑迪亚国家实验室以及众多公司合作。目前，他的咨询工作集中在自然语言网络代理的设计，以及分析海量数据集信息的深度学习技术。
卢格尔博士著有《人工智能：复杂问题求解的结构与策略》（艾迪生-韦斯利出版社，2009年，现已出版第六版）和《认识我们的世界》（施普林格出版社，2021年）人工智能。
中国人民大质性研究研修班（第一期）报名（点击下方海报链接）
中国人民大学质性研究研修班（第一期）致力于打造一个跨学科交流与深度对话的学术平台，探索质性研究在多元学科领域的理论创新与实践应用人工智能。本期研修班汇聚社会学、新闻学、法学、管理学、教育学、心理学、外国文学、历史学、经济学、人类学、信息学等不同学科背景的知名学者，从各自专业视角出发，共同探讨质性研究的方法论更新与学科边界拓展。研修班为期三天，设置十二场专题讲座，涵盖质性研究的基础理论、研究方法、实践应用和前沿议题。通过跨学科的视角碰撞与方法互鉴，研修班旨在推动不同领域质性研究的深度融合与创新发展。