Artificial Intelligence: A Modern Approach, 4/e (美國原版)
暫譯: 人工智慧：現代方法（第四版）

最全面、最新的人工智慧理論與實務介紹

期待已久的人工智慧：現代方法的修訂版探討了人工智慧（AI）領域的全貌與深度。第4版讓讀者了解最新技術，以更統一的方式呈現概念，並提供機器學習、深度學習、轉移學習、多智能體系統、機器人技術、自然語言處理、因果關係、機率程式設計、隱私、公平性及安全AI的新或擴展內容。

特色

提供最全面、最新的人工智慧理論與實務介紹

• 
  非技術性學習材料使用直觀的解釋介紹主要概念，然後再深入數學或演算法細節。非技術性語言使本書對更廣泛的讀者群可及。


• 
  統一的AI方法展示學生如何將AI的各個子領域結合起來，構建實際的、有用的程式。


• 
  更新 - AI系統的基本定義被概括化，以消除標準假設，即目標是固定且為智能代理所知；相反，代理可能對其所代表的人類的真實目標感到不確定。


• 
  深入涵蓋基本與進階主題，讓學生在不妥協複雜性和深度的情況下，對AI的前沿有基本了解。


• 
  作者維護的網站位於http://aima.cs.berkeley.edu/，包括與文本相關的評論和討論、練習、在線代碼庫、教師資源等！
  
  
  
  • 
    更新 - 互動式學生練習現在在網站上提供，以便持續更新和添加。
  
  
  • 
    更新 - 在線軟體為學生提供更多完成專案的機會，包括書中演算法的實現，以及Python、Java和JavaScript的補充編碼範例和應用。
  
  
  • 
    新 - 教學視頻教程加深學生的參與感，讓關鍵概念生動呈現。
  
  
  
  


• 
  靈活的格式使文本能夠適應不同教師的偏好。

跟上最新技術，並以更統一的方式呈現概念

• 
  新 - 新章節擴展了機率程式設計（第15章）；多智能體決策（第18章，與Michael Wooldridge合作）；深度學習（第21章，與Ian Goodfellow合作）；以及自然語言處理的深度學習（第24章，與Jacob Devlin和Mei-Wing Chang合作）。


• 
  更新 -增加了機器學習的涵蓋範圍。


• 
  更新 -有關機器人技術的材料顯著更新，包括與人類互動的機器人及強化學習在機器人技術中的應用。


• 
  新 -Judea Pearl撰寫的因果關係新章節。


• 
  新 -有關遊戲和機器人技術的蒙地卡羅搜尋的新章節。


• 
  新 -有關深度學習的轉移學習的新章節，涵蓋一般情況及自然語言。


• 
  新 -有關隱私、公平性、未來工作及安全AI的新章節。


• 
  新 -廣泛涵蓋AI應用的最新進展。


• 
  更新 -有關計算機視覺、自然語言理解和語音識別的修訂內容，反映深度學習方法對這些領域的影響。

本版新內容

提供最全面、最新的人工智慧理論與實務介紹

• 
  AI系統的基本定義被概括化，以消除標準假設，即目標是固定且為智能代理所知；相反，代理可能對其所代表的人類的真實目標感到不確定。


• 
  作者維護的網站位於http://aima.cs.berkeley.edu/，包括與文本相關的評論和討論、練習、在線代碼庫、教師資源等！
  
  
  
  • 
    互動式學生練習現在在網站上提供，以便持續更新和添加。
  
  
  • 
    更新的在線軟體為學生提供更多完成專案的機會，包括書中演算法的實現，以及Python、Java和JavaScript的補充編碼範例和應用。
  
  
  • 
    新的教學視頻教程加深學生的參與感，讓關鍵概念生動呈現。
  
  
  
  

跟上最新技術，並以更統一的方式呈現概念

• 
  新章節擴展了機率程式設計（第15章）；多智能體決策（第18章，與Michael Wooldridge合作）；深度學習（第21章，與Ian Goodfellow合作）；以及自然語言處理的深度學習（第24章，與Jacob Devlin和Mei-Wing Chang合作）。


• 增加了機器學習的涵蓋範圍。


• 有關機器人技術的材料顯著更新，包括與人類互動的機器人及強化學習在機器人技術中的應用。


• Judea Pearl撰寫的因果關係新章節。


• 有關遊戲和機器人技術的蒙地卡羅搜尋的新章節。


• 有關深度學習的轉移學習的新章節，涵蓋一般情況及自然語言。


• 有關隱私、公平性、未來工作及安全AI的新章節。


• 廣泛涵蓋AI應用的最新進展。


• 有關計算機視覺、自然語言理解和語音識別的修訂內容，反映深度學習方法對這些領域的影響。

Stuart Russell was born in 1962 in Portsmouth, England. He received his B.A. with first-class honours in physics from Oxford University in 1982, and his Ph.D. in computer science from Stanford in 1986. He then joined the faculty of the University of California at Berkeley, where he is a professor and former chair of computer science, director of the Center for Human-Compatible AI, and holder of the Smith–Zadeh Chair in Engineering. In 1990, he received the Presidential Young Investigator Award of the National Science Foundation, and in 1995 he was co-winner of the Computers and Thought Award. He is a Fellow of the American Association for Artificial Intelligence, the Association for Computing Machinery, and the American Association for the Advancement of Science, and Honorary Fellow of Wadham College, Oxford, and an Andrew Carnegie Fellow. He held the Chaire Blaise Pascal in Paris from 2012 to 2014. He has published over 300 papers on a wide range of topics in artificial intelligence. His other books include: The Use of Knowledge in Analogy and Induction, Do the Right Thing: Studies in Limited Rationality (with Eric Wefald), and Human Compatible: Artificial Intelligence and the Problem of Control.

Peter Norvig is currently Director of Research at Google, Inc., and was the director responsible for the core Web search algorithms from 2002 to 2005. He is a Fellow of the American Association for Artificial Intelligence and the Association for Computing Machinery. Previously, he was head of the Computational Sciences Division at NASA Ames Research Center, where he oversaw NASA’s research and development in artificial intelligence and robotics, and chief scientist at Junglee, where he helped develop one of the first Internet information extraction services. He received a B.S. in applied mathematics from Brown University and a Ph.D. in computer science from the University of California at Berkeley. He received the Distinguished Alumni and Engineering Innovation awards from Berkeley and the Exceptional Achievement Medal from NASA. He has been a professor at the University of Southern California and a research faculty member at Berkeley. His other books are: Paradigms of AI Programming: Case Studies in Common Lisp, Verbmobil: A Translation System for Face-to-Face Dialog, and Intelligent Help Systems for UNIX.

The two authors shared the inaugural AAAI/EAAI Outstanding Educator award in 2016.

斯圖亞特·拉塞爾（Stuart Russell）於1962年出生於英國朴茨茅斯。他於1982年獲得牛津大學物理學一級榮譽學士學位，並於1986年在史丹佛大學獲得計算機科學博士學位。隨後，他加入加州大學伯克利分校的教職，擔任計算機科學教授及前系主任，並擔任人類相容人工智慧中心的主任，以及史密斯-扎德工程學講座教授。1990年，他獲得美國國家科學基金會的總統青年研究員獎，1995年則共同獲得計算機與思維獎。他是美國人工智慧協會、計算機協會的會士，以及美國科學促進會的會士，並且是牛津大學瓦德漢學院的榮譽會士和安德魯·卡內基獎學金得主。他於2012年至2014年在巴黎擔任布萊斯·帕斯卡講座教授。他在人工智慧的各個主題上發表了300多篇論文。他的其他著作包括：《類比與歸納中的知識使用》（The Use of Knowledge in Analogy and Induction）、《做正確的事：有限理性的研究》（Do the Right Thing: Studies in Limited Rationality，與埃里克·維法德合著）以及《人類相容：人工智慧與控制問題》（Human Compatible: Artificial Intelligence and the Problem of Control）。

彼得·諾維格（Peter Norvig）目前是谷歌公司的研究總監，並於2002年至2005年負責核心網頁搜尋算法的主管。他是美國人工智慧協會和計算機協會的會士。此前，他曾擔任美國國家航空暨太空總署艾姆斯研究中心計算科學部的負責人，負責監督NASA在人工智慧和機器人技術方面的研究與開發，並擔任Junglee的首席科學家，協助開發首批互聯網信息提取服務之一。他在布朗大學獲得應用數學學士學位，並在加州大學伯克利分校獲得計算機科學博士學位。他曾獲得伯克利的傑出校友獎和工程創新獎，以及NASA的卓越成就獎章。他曾在南加州大學擔任教授，並在伯克利擔任研究教職。他的其他著作包括：《人工智慧編程的範式：Common Lisp的案例研究》（Paradigms of AI Programming: Case Studies in Common Lisp）、《Verbmobil：面對面對話的翻譯系統》（Verbmobil: A Translation System for Face-to-Face Dialog）以及《UNIX的智能幫助系統》（Intelligent Help Systems for UNIX）。

這兩位作者於2016年共同獲得首屆AAAI/EAAI傑出教育者獎。

Part 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  Risks and Benefits of AI
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
 
Part II: Problem Solving
3. Solving Problems by Searching
    3.1  Problem-Solving Agents
    3.2  Example Problems
    3.3  Search Algorithms
    3.4  Uninformed Search Strategies
    3.5  Informed (Heuristic) Search Strategies
    3.6  Heuristic Functions
4. Search in Complex Environments
    4.1  Local Search and Optimization Problems
    4.2  Local Search in Continuous Spaces
    4.3  Search with Nondeterministic Actions
    4.4  Search in Partially Observable Environments
    4.5  Online Search Agents and Unknown Environments
5. Adversarial Search and Games
    5.1  Game Theory
    5.2  Optimal Decisions in Games
    5.3  Heuristic Alpha--Beta Tree Search
    5.4  Monte Carlo Tree Search
    5.5  Stochastic Games
    5.6  Partially Observable Games
    5.7  Limitations of Game Search Algorithms
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
 
Part III: Knowledge and Reasoning
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
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
9. Inference in First-Order Logic
    9.1  Propositional vs.~First-Order Inference
    9.2  Unification and First-Order Inference
    9.3  Forward Chaining
    9.4  Backward Chaining
    9.5  Resolution
10. Knowledge Representation
    10.1  Ontological Engineering
    10.2  Categories and Objects
    10.3  Events
    10.4  Mental Objects and Modal Logic
    10.5  Reasoning Systems for Categories
    10.6  Reasoning with Default Information
11. Automated Planning
    11.1  Definition of Classical Planning
    11.2  Algorithms for Classical Planning
    11.3  Heuristics for Planning
    11.4  Hierarchical Planning
    11.5  Planning and Acting in Nondeterministic Domains
    11.6  Time, Schedules, and Resources
    11.7  Analysis of Planning Approaches
12. Quantifying Uncertainty
    12.1  Acting under Uncertainty
    12.2  Basic Probability Notation
    12.3  Inference Using Full Joint Distributions
    12.4  Independence
    12.5  Bayes' Rule and Its Use
    12.6  Naive Bayes Models
    12.7  The Wumpus World Revisited
 
Part IV: Uncertain Knowledge and Reasoning
13. Probabilistic Reasoning
    13.1  Representing Knowledge in an Uncertain Domain
    13.2  The Semantics of Bayesian Networks
    13.3  Exact Inference in Bayesian Networks
    13.4  Approximate Inference for Bayesian Networks
    13.5  Causal Networks
14. Probabilistic Reasoning over Time
    14.1  Time and Uncertainty
    14.2  Inference in Temporal Models
    14.3  Hidden Markov Models
    14.4  Kalman Filters
    14.5  Dynamic Bayesian Networks
15. Probabilistic Programming
    15.1  Relational Probability Models
    15.2  Open-Universe Probability Models
    15.3  Keeping Track of a Complex World
    15.4  Programs as Probability Models
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  Unknown Preferences
17. Making Complex Decisions
    17.1  Sequential Decision Problems
    17.2  Algorithms for MDPs
    17.3  Bandit Problems
    17.4  Partially Observable MDPs
    17.5  Algorithms for solving POMDPs
 
Part V: Learning
18. Multiagent Decision Making
    18.1  Properties of Multiagent Environments
    18.2  Non-Cooperative Game Theory
    18.3  Cooperative Game Theory
    18.4  Making Collective Decisions
19. Learning from Examples
    19.1  Forms of Learning
    19.2  Supervised Learning
    19.3  Learning Decision Trees
    19.4  Model Selection and Optimization
    19.5  The Theory of Learning
    19.6  Linear Regression and Classification
    19.7  Nonparametric Models
    19.8  Ensemble Learning
    19.9  Developing Machine Learning Systems
20. Learning Probabilistic Models
    20.1  Statistical Learning
    20.2  Learning with Complete Data
    20.3  Learning with Hidden Variables: The EM Algorithm
21. Deep Learning
    21.1  Simple Feedforward Networks
    21.2  Mixing and matching models, loss functions and optimizers
    21.3  Loss functions
    21.4  Models
    21.5  Optimization Algorithms
    21.6  Generalization
    21.7  Recurrent neural networks
    21.8  Unsupervised, semi-supervised and transfer learning
    21.9  Applications
 
Part VI: Communicating, Perceiving, and Acting
22. Reinforcement Learning
    22.1  Learning from Rewards
    22.2  Passive Reinforcement Learning
    22.3  Active Reinforcement Learning
    22.4  Safe Exploration
    22.5  Generalization in Reinforcement Learning
    22.6  Policy Search
    22.7  Applications of Reinforcement Learning
23. Natural Language Processing
    23.1  Language Models
    23.2  Grammar
    23.3  Parsing
    23.4  Augmented Grammars
    23.5  Complications of Real Natural Language
    23.6  Natural Language Tasks
24. Deep Learning for Natural Language Processing
    24.1  Limitations of Feature-Based NLP Models
    24.2  Word Embeddings
    24.3  Recurrent Neural Networks
    24.4  Sequence-to-sequence Models
    24.5  The Transformer Architecture
    24.6  Pretraining and Transfer Learning
    24.7  Introduction
    24.8  Image Formation
    24.9  Simple Image Features
    24.10 Classifying Images
    24.11 Detecting Objects
    24.12 The 3D World
    24.13 Using Computer Vision
25. Robotics
    25.1  Robots
    25.2  Robot Hardware
    25.3  What kind of problem is robotics solving?
    25.4  Robotic Perception
    25.5  Planning and Control
    25.6  Planning Uncertain Movements
    25.7  Reinforcement Learning in Robotics
    25.8  Humans and Robots
    25.9  Alternative Robotic Frameworks
    25.10 Application Domains
 
Part VII: Conclusions
26. Philosophy and Ethics of AI
    26.1  Weak AI: What are the Limits of AI?
    26.2  Strong AI: Can Machines Really Think?
    26.3  The Ethics of AI
27. The Future of AI
    27.1  AI Components
    27.2  AI Architectures
 

Appendix A: Mathematical Background
    A.1  Complexity Analysis and O() Notation
    A.2  Vectors, Matrices, and Linear Algebra
    A.3  Probability Distributions
Appendix B: Notes on Languages and Algorithms
    B.1  Defining Languages with Backus--Naur Form (BNF)
    B.2  Describing Algorithms with Pseudocode
    B.3  Online Supplemental Material

Part 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  Risks and Benefits of AI

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

 

Part II: Problem Solving

3. Solving Problems by Searching

    3.1  Problem-Solving Agents

    3.2  Example Problems

    3.3  Search Algorithms

    3.4  Uninformed Search Strategies

    3.5  Informed (Heuristic) Search Strategies

    3.6  Heuristic Functions

4. Search in Complex Environments

    4.1  Local Search and Optimization Problems

    4.2  Local Search in Continuous Spaces

    4.3  Search with Nondeterministic Actions

    4.4  Search in Partially Observable Environments

    4.5  Online Search Agents and Unknown Environments

5. Adversarial Search and Games

    5.1  Game Theory

    5.2  Optimal Decisions in Games

    5.3  Heuristic Alpha--Beta Tree Search

    5.4  Monte Carlo Tree Search

    5.5  Stochastic Games

    5.6  Partially Observable Games

    5.7  Limitations of Game Search Algorithms

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

 

Part III: Knowledge and Reasoning

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

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

9. Inference in First-Order Logic

    9.1  Propositional vs.~First-Order Inference

    9.2  Unification and First-Order Inference

    9.3  Forward Chaining

    9.4  Backward Chaining

    9.5  Resolution

10. Knowledge Representation

    10.1  Ontological Engineering

    10.2  Categories and Objects

    10.3  Events

    10.4  Mental Objects and Modal Logic

    10.5  Reasoning Systems for Categories

    10.6  Reasoning with Default Information

11. Automated Planning

    11.1  Definition of Classical Planning

    11.2  Algorithms for Classical Planning

    11.3  Heuristics for Planning

    11.4  Hierarchical Planning

    11.5  Planning and Acting in Nondeterministic Domains

    11.6  Time, Schedules, and Resources

    11.7  Analysis of Planning Approaches

12. Quantifying Uncertainty

    12.1  Acting under Uncertainty

    12.2  Basic Probability Notation

    12.3  Inference Using Full Joint Distributions

    12.4  Independence

    12.5  Bayes' Rule and Its Use

    12.6  Naive Bayes Models

    12.7  The Wumpus World Revisited

 

Part IV: Uncertain Knowledge and Reasoning

13. Probabilistic Reasoning

    13.1  Representing Knowledge in an Uncertain Domain

    13.2  The Semantics of Bayesian Networks

    13.3  Exact Inference in Bayesian Networks

    13.4  Approximate Inference for Bayesian Networks

    13.5  Causal Networks

14. Probabilistic Reasoning over Time

    14.1  Time and Uncertainty

    14.2  Inference in Temporal Models

    14.3  Hidden Markov Models

    14.4  Kalman Filters

    14.5  Dynamic Bayesian Networks

15. Probabilistic Programming

    15.1  Relational Probability Models

    15.2  Open-Universe Probability Models

    15.3  Keeping Track of a Complex World

    15.4  Programs as Probability Models

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  Unknown Preferences

17. Making Complex Decisions

    17.1  Sequential Decision Problems

    17.2  Algorithms for MDPs

    17.3  Bandit Problems

    17.4  Partially Observable MDPs

    17.5  Algorithms for solving POMDPs

 

Part V: Learning

18. Multiagent Decision Making

    18.1  Properties of Multiagent Environments

    18.2  Non-Cooperative Game Theory

    18.3  Cooperative Game Theory

    18.4  Making Collective Decisions

19. Learning from Examples

    19.1  Forms of Learning

    19.2  Supervised Learning

    19.3  Learning Decision Trees

    19.4  Model Selection and Optimization

    19.5  The Theory of Learning

    19.6  Linear Regression and Classification

    19.7  Nonparametric Models

    19.8  Ensemble Learning

    19.9  Developing Machine Learning Systems

20. Learning Probabilistic Models

    20.1  Statistical Learning

    20.2  Learning with Complete Data

    20.3  Learning with Hidden Variables: The EM Algorithm

21. Deep Learning

    21.1  Simple Feedforward Networks

    21.2  Mixing and matching models, loss functions and optimizers

    21.3  Loss functions

    21.4  Models

    21.5  Optimization Algorithms

    21.6  Generalization

    21.7  Recurrent neural networks

    21.8  Unsupervised, semi-supervised and transfer learning

    21.9  Applications

 

Part VI: Communicating, Perceiving, and Acting

22. Reinforcement Learning

    22.1  Learning from Rewards

    22.2  Passive Reinforcement Learning

    22.3  Active Reinforcement Learning

    22.4  Safe Exploration

    22.5  Generalization in Reinforcement Learning

    22.6  Policy Search

    22.7  Applications of Reinforcement Learning

23. Natural Language Processing

    23.1  Language Models

    23.2  Grammar

    23.3  Parsing

    23.4  Augmented Grammars

    23.5  Complications of Real Natural Language

    23.6  Natural Language Tasks

24. Deep Learning for Natural Language Processing

    24.1  Limitations of Feature-Based NLP Models

    24.2  Word Embeddings

    24.3  Recurrent Neural Networks

    24.4  Sequence-to-sequence Models

    24.5  The Transformer Architecture

    24.6  Pretraining and Transfer Learning

    24.7  Introduction

    24.8  Image Formation

    24.9  Simple Image Features

    24.10 Classifying Images

    24.11 Detecting Objects

    24.12 The 3D World

    24.13 Using Computer Vision

25. Robotics

    25.1  Robots

    25.2  Robot Hardware

    25.3  What kind of problem is robotics solving?

    25.4  Robotic Perception

    25.5  Planning and Control

    25.6  Planning Uncertain Movements

    25.7  Reinforcement Learning in Robotics

    25.8  Humans and Robots

    25.9  Alternative Robotic Frameworks

    25.10 Application Domains

 

Part VII: Conclusions

26. Philosophy and Ethics of AI

    26.1  Weak AI: What are the Limits of AI?

    26.2  Strong AI: Can Machines Really Think?

    26.3  The Ethics of AI

27. The Future of AI

    27.1  AI Components

    27.2  AI Architectures

 

Appendix A: Mathematical Background

    A.1  Complexity Analysis and O() Notation

    A.2  Vectors, Matrices, and Linear Algebra

    A.3  Probability Distributions

Appendix B: Notes on Languages and Algorithms

    B.1  Defining Languages with Backus--Naur Form (BNF)

    B.2  Describing Algorithms with Pseudocode

    B.3  Online Supplemental Material