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The most comprehensive, up-to-date introduction to the theory and practice of artificial intelligence
The long-anticipated revision of Artificial Intelligence: A Modern Approach explores the full breadth and depth of the field of artificial intelligence (AI). The 4th Edition brings readers up to date on the latest technologies, presents concepts in a more unified manner, and offers new or expanded coverage of machine learning, deep learning, transfer learning, multiagent systems, robotics, natural language processing, causality, probabilistic programming, privacy, fairness, and safe AI.
本書特色
Offer the most comprehensive, up-to-date introduction to the theory and practice of artificial intelligence
． Nontechnical learning material introduces major concepts using intuitive explanations, before going into mathematical or algorithmic details. The nontechnical language makes the book accessible to a broader range of readers.
． A unified approach to AI shows students how the various subfields of AI fit together to build actual, useful programs.
． UPDATED - The basic definition of AI systems is generalized to eliminate the standard assumption that the objective is fixed and known by the intelligent agent; instead, the agent may be uncertain about the true objectives of the human(s) on whose behalf it operates.
． In-depth coverage of both basic and advanced topics provides students with a basic understanding of the frontiers of AI without compromising complexity and depth.
． The Author-Maintained Website at http://aima.cs.berkeley.edu/ includes text-related comments and discussions, exercises, an online code repository, Instructor Resources, and more!
． UPDATED - Interactive student exercises are now featured on the website to allow for continuous updating and additions.
． UPDATED - Online software gives students more opportunities to complete projects, including implementations of the algorithms in the book, plus supplemental coding examples and applications in Python, Java, and Javascript.
． NEW - Instructional video tutorials deepen students’ engagement and bring key concepts to life.
． A flexible format makes the text adaptable for varying instructors' preferences.
Stay current with the latest technologies and present concepts in a more unified manner
． NEW - New chapters feature expanded coverage of probabilistic programming (Ch. 15); multiagent decision making (Ch. 18 with Michael Wooldridge); deep learning (Ch. 21 with Ian Goodfellow); and deep learning for natural language processing (Ch. 24 with Jacob Devlin and Mei-Wing Chang).
． UPDATED - Increased coverage of machine learning.
． UPDATED - Significantly updated material on robotics includes robots that interact with humans and the application of reinforcement learning to robotics.
． NEW - New section on causality by Judea Pearl.
． NEW - New sections on Monte Carlo search for games and robotics.
． NEW - New sections on transfer learning for deep learning in general and for natural language.
． NEW - New sections on privacy, fairness, the future of work, and safe AI.
． NEW - Extensive coverage of recent advances in AI applications.
． UPDATED - Revised coverage of computer vision, natural language understanding, and speech recognition reflect the impact of deep learning methods on these fields.
作者簡介
Stuart Russell
加州大學柏克萊分校計算機科學教授、加州大學舊金山分校神經外科兼任教授
Peter Norvig
現為Google公司研究總監
I Artificial Intelligence
1 Introduction
2 Intelligent Agents
II Problem-solving
3 Solving Problems by Searching
4 Search in Complex Environments
5 Constraint Satisfaction Problems
6 Adversarial Search and Games
III Knowledge, reasoning, and planning
7 Logical Agents
8 First-Order Logic
9 Inference in First-Order Logic
10 Knowledge Representation
11 Automated Planning
IV Uncertain knowledge and reasoning
12 Quantifying Uncertainty
13 Probabilistic Reasoning
14 Probabilistic Reasoning over Time
15 Making Simple Decisions
16 Making Complex Decisions
17 Multiagent Decision Making
18 Probabilistic Programming
V Machine Learning
19 Learning from Examples
20 Knowledge in Learning
21 Learning Probabilistic Models
22 Deep Learning
23 Reinforcement Learning
VI Communicating, perceiving, and acting
24 Natural Language Processing
25 Deep Learning for Natural Language Processing
26 Robotics
27 Computer Vision
VII Conclusions
28 Philosophy, Ethics, and Safety of AI
29 The Future of AI
Appendix A: Mathematical Background
Appendix B: Notes on Languages and Algorithms
Bibliography
Index
