人工智能
2011-7
清华大学出版社
Stuart J. Russell,Peter Norvig
1132
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《人工智能(一种现代的方法第3版影印版》(作者拉塞尔、诺维格)是最权威、最经典的人工智能教材，已被全世界100多个国家的1200多所大学用作教材。
《人工智能(一种现代的方法第3版影印版》的最新版全面而系统地介绍了人工智能的理论和实践，阐述了人工智能领域的核心内容，并深入介绍了各个主要的研究方向。全书仍分为八大部分：第一部分“人工智能”，第二部分“问题求解”，第三部分“知识与推理”，第四部分“规划”，第五部分“不确定知识与推理”，第六部分“学习”，第七部分“通信、感知与行动”，第八部分“结论”。《人工智能(一种现代的方法第3版影印版》既详细介绍了人工智能的基本概念、思想和算法，还描述了其各个研究方向最前沿的进展，同时收集整理了详实的历史文献与事件。另外，《人工智能(一种现代的方法第3版影印版》的配套网址为教师和学生提供了大量教学和学习资料。
《人工智能(一种现代的方法第3版影印版》适合于不同层次和领域的研究人员及学生，是高等院校本科生和研究生人工智能课的首选教材，也是相关领域的科研与工程技术人员的重要参考书。
作者：（美国）拉塞尔（Stuart J.Russell） （美国）诺维格（Peter Norvig）
I Artificial Intelligence
1 Introduction
1.1 What Is AI?
1.2 The Foundations of Artificial Intelligence
1.3 The History of Artificial Intelligence
1.4 The State of the Art
1.5 Summary, Bibliographical and Historical Notes, Exercises
2 Intelligent Agents
2.1 Agents and Environments
2.2 Good Behavior: The Concept of Rationality
2.3 The Nature of Environments
2.4 The Structure of Agents
2.5 Summary, Bibliographical and Historical Notes, Exercises
II Problem-solving
3 Solving Problems by Searching
3.1 Problem-Solving Agents
3.2 Example Problems r
3.3 Searching for Solutions
3.4 Uninformed Search Strategies
3.5 Informed (Heuristic) Search Strategies
3.6 Heuristic Functions
3.7 Summary, Bibliographical and Historical Notes, Exercises
4 Beyond Classical Search
4.1 Local Search Algorithms and Optimization Problems
4.2 Local Search in Continuous Spaces
4.3 Searching with Nondeterministic Actions
4.4 Searching with Partial Observations
4.5 Online Search Agents and Unknown Environments
4.6 Summary, Bibliographical and Historical Notes, Exercises
5 Adversariai Search
5.1 Games
5.2 Optimal Decisions in Games
5.3 Alpha-Beta Pruning
5.4 Imperfect Real-Time Decisions
5.5 Stochastic Games
5.6 Partially Observable Games
5.7 State-of-the-Art Game Programs
5.8 Alternative Approaches
5.9 Summary, Bibliographical and Historical Notes, Exercises
6 Constraint Satisfaction Problems
6.1 Defining Constraint Satisfaction Problems
6.2 Constraint Propagation: Inference in CSPs
6.3 Backtracking Search for CSPs
6.4 Local Search for CSPs
6.5 The Structure of Problems
6.6 Summary, Bibliographical and Historical Notes, Exercises
III Knowledge, reasoning, and planning
7 Logical Agents
7.1 Knowledge-Based Agents
7.2 The Wumpus World
7.3 Logic
7.4 Propositional Logic: A Very Simple Logic
7.5 Propositional Theorem Proving
7.6 Effective Propositional Model Checking
7.7 Agents Based on Propositional Logic
7.8 Summary, Bibliographical and Historical Notes, Exercises
8 First-Order Logic
8.1 Representation Revisited
8.2 Syntax and Semantics of First-Order Logic
8.3 Using First-Order Logic.
8.4 Knowledge Engineering in First-Order Logic
8.5 Summary, Bibliographical and Historical Notes, Exercises
9 Inference in First-Order Logic
9.1 Propositional vs. First-Order Inference
9.2 Unification and Lifting
9.3 Forward Chaining
9.4 Backward Chaining
9.5 Resolution
9.6 Summary, Bibliographical and Historical Notes, Exer-cises
10 Classical Planning
10.1 Definition of Classical Planning
10.2 Algorithms for Planning as State-Space Search
10.3 Planning Graphs
10.4 Other Classical Planning Approaches
10.5 Analysis of Planning Approaches
10.6 Summary, Bibliographical and Historical Notes, Exercises
11 Planning and Acting in the Real World
11.1 Time,. Schedules, and Resources
11.2 Hierarchical Planning
11.3 Planning and Acting in Nondeterministic Domains
11.4 Multiagent Planning
11.5 Summary, Bibliographical and Historical Notes, Exercises
12 Knowledge Representation
12.1 Ontological Engineering
12.2 Categories and Objects
12.3 Events
12.4 Mental Events and Ment.al Objects
12.5 Reasoning Systems for Categories
12.6 Reasoning with Default Information
12.7 The Internet Shopping World
12.8 Summary, Bibliographical and Historical Notes, Exercises
IV Uncertain knowledge and reasoning
13 Quantifying Uncertainty
13.1 Acting under Uncertainty
13.2 Basic Probability Notation
13.3 Inference Using Full Joint Distributions
13.4 Independence
13.5 Bayes' Rule and Its Use
13.6 The Wumpus World Revisited
13.7 Summary, Bibliographical and Historical Notes, Exercises
14 Probabilistic Reasoning
14.1 Representing Knowledge in an Uncertain Domain
14.2 The Semantics of Bayesian Networks
14.3 Efficient Representation of Conditional Distributions
14.4 Exact Inference in Bayesian Networks
14.5 Approximate Inference in Bayesian Networks
14.6 Relational and First-Order Probability Models
14.7 Other Approaches to Uncertain ReasOning
14.8 Summary, Bibliographical and Historical Notes, Exercises
15 Probabilistic Reasoning over Time
15.1 Time and Uncertainty
15.2 Inference in Temporal Models
15.3 Hidden Markov Models
15.4 Kalman Filters
15.5 Dynamic Bayesian Networks
15.6 Keeping Track of Many Objects
15.7 Summary, Bibliographical and Historical Notes, Exercises
16 Making Simple Decisions
16.1 Combining Beliefs and Desires under Uncertainty
16.2 The Basis of Utility Theory
16.3 Utility Functions
16.4 Multiattribute Utility Functions
16.5 Decision Networks
16.6 The Value of Information
16.7 Decision-Theoretic Expert Systems
16.8 Summary, Bibliographical and Historical Notes, Exercises
17 Making Complex Decisions
17.1 Sequential Decision Problems
17.2 Value Iteration
17.3 Policy Iteration
17.4 Partially Observable MDPs
17.5 Decisions with Multiple Agents: Game Theory
17.6 Mechanism Design
17.7 Summary, Bibliographical and Historical Notes, Exercises
V Learning
18 Learning from Examples
18.1 Forms of Learning
18.2 Supervised Learning
18.3 Learning Decision Trees
18.4 Evaluating and Choosing the Best Hypothesis
18.5 The Theory of Learning
18.6 Regression and:Classification with Linear Models
18.7 Artificial Neural Networks
18.8 Nonparametric Models
18.9 Support Vector Machines
18.10 Ensemble Learning
18. I 1 Practical Machine Learning
18.12 Summary, Bibliographical and Historical Notes, Exercises
19 Knowledge in Learning
19.1 A Logical Formulation of Learning
19.2 Knowledge in Learning
19.3 Explanation-Based Learning
19.4 Learning Using Relevance Information
19.5 Inductive Logic Programming
19.6 Summary, Bibliographical and Historical Notes, Exercises
20 Learning Probabilistic Models
20:1 Statistical Learning
20.2 Learning with Complete' Data
20.3 Learning with Hidden Variables: The EM Algorithm
20.4 Summary, Bibliographical and Historical Notes, Exercises
21 Reinforcement Learning
21.1 Introduction
21.2 Passive Reinforcement Learning
21.3 Active Reinforcement Learning
21.4 Generalization in Reinforcement Learning
21.5 Policy Searcti
21.6 Applications of Reinforcement Learning
21.7 Summary, Bibliographical and Historical Notes, Exercises
VI Communicating, perceiving, and acting
22 Natural Language Pi'ocessing
22.1 Language Models
22.2 Text Classification
22.3 Information Retrieval
22.4 Information Extraction
22.5 Summary, Bibliographical and Historical Notes, Exercises
23 Natural Language for Communication
23.1 Phrase Structure Grammars
23.2 Syntactic Analysis (Parsing)
23.3 Augmented Grammars and Semantic Interpretation
23.4 Machine Translation
23.5 Speech Recognition
23.6 Summary, Bibliographical and Historical Notes, Exercises
24 Perception
24.1 Image Formation
24.2 Early Image-Processing Operations
24.3 Object Recognition by Appearance
24.4 Reconstructing the3D World
24.5 Object Recognition from Structural Information
24.6 .Using Vision
24.7 Summary, Bibliographical and Histiarical Notes, Exercises
25 Robotics
25.1 Introduction
25.2 Robot Hardware
25.3 Robotic Perception
25.4 Planning to Move
25.5 Planning Uncertain Movements
25.6 Moving
25.7 Robotic Software Architectures
25.8 Application Domains .
25.9 Summary, Bibliographical and Historical Notes, Exercises
VII Conclusions
26 Philosophical Foundations
26.1 Weak AI: Can Machines Act Intelligently?
26.2 Strong AI: Can Machines Really Think?
26.3 The Ethics and Risks of Developing Artificial Intelligence
26.4 Summary, Bibliographical and Historical Notes, Exercises
27 AI: The Present and Future
27.1 Agent Components
27.2 Agent Architectures
27.3 Are We Going in the Right Direction?
27.4 What If AI Does Succeed?
A Mathematical background
A. 1 Complexity Analysis and O0 Notation
A.2 Vectors, Matrices, and Linear Algebra
A.3 Probability Distributions
B Notes on Languages and Algorithms
B.1 Defining Languages with Backus-Naur Form (BNF)
B.2 Describing Algorithms with Pseudocode
B.3 Online Help
Bibliography
Index
版权页：插图：The last component of the learning agent is the problem generator. It is responsible for suggesting actions that will lead to new and informative experiences. The point is that if the performance element had its way, it would keep doing the actions that are best, given what it knows. But if the agent is willing to explore a little and do some perhaps suboptimal actions in the short run, it might discover much better actions for the long run. The problem generator's job is to suggest these exploratory actions. This is what scientists do when they carry out experiments. Galileo did not think that dropping rocks from the top of a tower in Pisa was valuable in itself. He was not trying to break the rocks or to modify the brains of unfortunate passers-by. His aim was to modify his own brain by identifying a better theory of the motion of objects.To make the overall design more concrete, let us return to the automated taxi example. The performance element consists of whatever collection of knowledge and procedures the taxi has for selecting its driving actions. The taxi goes out on the road and drives, using this performance element. The critic observes the world and passes information along to the learning element. For example, after the taxi makes a quick left turn across three lanes of traffic, the critic observes the shocking language used by other drivers. From this experience, the learning element is able to formulate a rule saying this was a bad action, and the performance element is modified by installation of the new rule. The problem generator might identify certain areas of behavior in need of improvement and suggest experiments, such as trying out the brakes on different road surfaces under different conditions.The learning element can make changes to any of the "knowledge" components shown in the agent diagrams (Figures 2.9, 2.11, 2.13, and 2.14). The simplest cases involve learning directly from the percept sequence. Observation of pairs of successive states of the environment can allow the agent to learn "How the world evolves," and observation of the results of its actions can allow the agent to learn "What my actions do." For example, if the taxi exerts a certain braking pressure when driving on a wet road, then it will soon find out how much deceleration is actually achieved. Clearly, these two learning tasks are more difficult if the environment is only partially observable.
《人工智能:一种现代的方法(第3版)(影印版)》为大学计算机教育国外著名教材系列之一。
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人工智能：一种现代的方法
国外人工智能不可多得的教材，值得深入研究
很喜欢这本书。看了目录，扫看了一些章节，感觉很适合初学者系统的了解和学习人工智能系统理论、概念。
人工智能的经典书籍。
书很厚，涉及到统计模型、逻辑推理、知识表达，写的比较细。
先进的理念，先进的广法，还是英文，好好学习，实力提升大大的！
内容丰富，是一本很好的教科书
书到了，是英文版的，书很厚，内容很好
书很好，我看了好多人推荐的
好书，就是需要慢慢的读。
挺好的一本书。。挺好的一本书。。
目前看到最好的一本书。
好东西，书上有很多！
送货速度很快，谢谢啦
很好，全英文的
非常好的一本书！有待于研究中！很考验一个人的英语水平！
讲得很全吧，建议结合网站精读之
好书啊，不知什么时候能看完
书写的很全，一千多页~~~
书质量不错 顶下
我敢肯定，最后一本是我买到的。哈哈！
大师的名作！智慧的结晶！必读！
从Agent角度把人工智能内容统一起来是这本教材的历史功绩。当然从有些内容方面（如知识表示），还需要其他一些书来补充。
网上学习课程时需要用到本书，应该还是不错的。
那么贵的书，纸张却用的越来越差。。不过书确实是经典中的经典，4星是
给书的质量的，书内容的质量绝对是5星
学AI，professor指定的，质量挺不错，很厚的一本书。内容很难。。。。。
书被淋湿了一角，而且还是我等不急了直接打电话给快递员的 跟我说差点忘了...
英文的还没有看，但是这本书是公认的人工智能领域的巨著
内容绝对是经典中的经典，但是纸张质量和印刷质量实在不敢恭维。纸非常的薄，封面也是很软的那种，中间也不怎么白，感觉质量还不如人民邮电出的第二版，但价格却将近那个的两倍……如果大家不介意版次的话，我建议大家去买那本好了，内容没有什么大的差别……
是教科书来的，但是上课从来不看...只用看PPT就好了...这书死重死重买来放宿舍占地方啊...
经典中的经典，推荐！
值得拥有，这类书籍里面内容都很不错。用于教学帮助挺大。
慢慢看，正在消化中。虽然是英文版但是看起来不是很费劲
好！不愧是大牛的作品，就是太厚重了。
不过我还是试图将它读完
非常值得入手的一本书。个人喜欢人工智能的原因是想了解思维的形成与改进推理的过程。
书太厚了，估计这门课上完也只能读1/3
在几本AI教材间犹豫后，选定这本。先看了电子版，再买的实版。全英，印刷上乘，砖头厚，携带起来是不方便的，但这就是经典！
快递很给力，纸张质量没有达到期望值，内容当然是没话说，经典中的经典。
头天下单，第二天上午就送货了。书很厚，纸张和印刷质量还可以。装订处的胶有小空洞，希望不要脱胶。
总体还可以，就是可能由于书太厚，中间有些纸张没粘合好。。。
挺不错的,就是有点小贵^
书确实经典
还没读，希望不错啊
帮别人买的，还算不错吧
圣经的教科书
方便快捷，送货上门，不错