Artificial Intelligence: A Modern Approach, 3/e (GE-Paperback)

Stuart Russell , Peter Norvig

Artificial Intelligence: A Modern Approach, 3/e (GE-Paperback)

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商品描述

Description

 

For one or two-semester, undergraduate or graduate-level courses in Artificial Intelligence.

The long-anticipated revision of this best-selling text offers the most comprehensive, up-to-date introduction to the theory and practice of artificial intelligence.

 

Features

 

  • Nontechnical learning material.

    • Provides a simple overview of major concepts, uses a nontechnical language to help increase understanding. Makes the book accessible to a broader range of students.

  • The Internet as a sample application for intelligent systems — Examples of logical reasoning, planning, and natural language processing using Internet agents.

    • Promotes student interest with interesting, relevant exercises.

  • Increased coverage of material — New or expanded coverage of constraint satisfaction, local search planning methods, multi-agent systems, game theory, statistical natural language processing and uncertain reasoning over time. More detailed descriptions of algorithms for probabilistic inference, fast propositional inference, probabilistic learning approaches including EM, and other topics.

    • Brings students up to date on the latest technologies, and presents concepts in a more unified manner.

  • Updated and expanded exercises — 30% of the exercises are revised or NEW.
  • More Online Software.

    • Allows many more opportunities for student projects on the web.

  • A unified, agent-based approach to AI — Organizes the material around the task of building intelligent agents.

    • Shows students how the various subfields of AI fit together to build actual, useful programs.

  • Comprehensive, up-to-date coverage — Includes a unified view of the field organized around the rational decision making paradigm.
  • A flexible format.

    • Makes the text adaptable for varying instructors' preferences.

  • In-depth coverage of basic and advanced topics.

    • Provides students with a basic understanding of the frontiers of AI without compromising complexity and depth.

  • Pseudo-code versions of the major AI algorithms are presented in a uniform fashion, and Actual Common Lisp and Python implementations of the presented algorithms are available via the Internet.

    • Gives instructors and students a choice of projects; reading and running the code increases understanding.

  • Author Maintained Website

    • Visit http://aima.cs.berkeley.edu/ to access text-related Comments and Discussions, AI Resources on the Web, and Online Code Repository, Instructor Resources, and more!

 

New to this Edition

This edition captures the changes in AI that have taken place since the last edition in 2003. There have been important applications of AI technology, such as the widespread deployment of practical speech recognition, machine translation, autonomous vehicles, and household robotics. There have been algorithmic landmarks, such as the solution of the game of checkers. And there has been a great deal of theoretical progress, particularly in areas such as probabilistic reasoning, machine learning, and computer vision. Most important from the authors' point of view is the continued evolution in how we think about the field, and thus how the book is organized. The major changes are as follows:

  • More emphasis is placed on partially observable and nondeterministic environments, especially in the nonprobabilistic settings of search and planning. The concepts of belief state (a set of possible worlds) and state estimation (maintaining the belief state) are introduced in these settings; later in the book, probabilities are added.
  • In addition to discussing the types of environments and types of agents, there is more in more depth coverage of the types of representations that an agent can use. Differences between atomic representations (in which each state of the world is treated as a black box), factored representations (in which a state is a set of attribute/value pairs), and structured representations (in which the world consists of objects and relations between them) are distinguished.
  • Coverage of planning goes into more depth on contingent planning in partially observable environments and includes a new approach to hierarchical planning.
  • New material on first-order probabilistic models is added, including open-universe models for cases where there is uncertainty as to what objects exist.
  • The introductory machine-learning chapter is completely rewritten, stressing a wider variety of more modern learning algorithms and placing them on a firmer theoretical footing.
  • Expanded coverage of Web search and information extraction, and of techniques for learning from very large data sets.
  • 20% of the citations in this edition are to works published after 2003.
  • Approximately 20% of the material is brand new. The remaining 80% reflects older work but is largely rewritten to present a more unified picture of the field.

商品描述(中文翻譯)

描述

 

適用於一學期或兩學期的人工智慧本科或研究生課程。

這本暢銷教材的期待已久的修訂版提供了最全面、最新的人工智慧理論和實踐介紹。

 

特點

 



  • 非技術性學習材料。



    • 提供主要概念的簡單概述，使用非技術性語言以增加理解。使本書更容易被更廣泛的學生接觸。







  • 以互聯網作為智能系統的樣本應用 — 使用互聯網代理進行邏輯推理、規劃和自然語言處理的示例。



    • 通過有趣、相關的練習提高學生的興趣。







  • 增加了更多的內容 — 新增或擴展了對約束滿足、局部搜索規劃方法、多智能體系統、博弈論、統計自然語言處理和不確定推理等內容的覆蓋。更詳細地描述了概率推理、快速命題推理、概率學習方法（包括EM）等算法。



    • 使學生了解最新技術，並以更統一的方式呈現概念。







  • 更新和擴展的練習 — 30%的練習被修訂或新增。



  • 更多的在線軟件。



    • 為學生在網絡上進行更多的項目提供了更多機會。







  • 統一的基於代理的AI方法 — 將材料組織在建立智能代理的任務周圍。



    • 向學生展示AI的各個子領域如何結合在一起建立實際有用的程序。







  • 全面、最新的覆蓋範圍 — 包括圍繞理性決策模型組織的領域的統一觀點。



  • 靈活的格式。



    • 使教材適應不同教師的偏好。







  • 深入涵蓋基礎和高級主題。



    • 為學生提供對AI前沿的基本理解，同時不妥協於複雜性和深度。







  • 以統一的方式呈現主要AI算法的偽代碼版本，並通過互聯網提供實際的Common Lisp和Python實現。



    • 為教師和學生提供了選擇項目的機會；閱讀和運行代碼可以增加理解。







  • 作者維護的網站



    • 訪問http://aima.cs.berkeley.edu/以獲取與文本相關的評論和討論、網絡上的AI資源和在線代碼庫、教師資源等等！





 

本版的新特點

本版反映了自2003年上一版以來人工智慧領域的變化。人工智慧技術已經有了重要的應用，例如實用語音識別、機器翻譯、自動駕駛車輛和家庭機器人的廣泛部署。算法上也有了重要的突破，例如解決了跳棋遊戲。還有一些重要的新發現和進展。

目錄大綱

Table of Contents

 

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

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. Adversarial 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, Exercises

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 Mental 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

18.11 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 Search

21.6 Applications of Reinforcement Learning

21.7 Summary, Bibliographical and Historical Notes, Exercises

VI. Communicating, Perceiving, and Acting

22. Natural Language Processing

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 the 3D World

24.5 Object Recognition from Structural Information

24.6 Using Vision

24.7 Summary, Bibliographical and Historical 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?

Appendices

A. Mathematical Background

A.1 Complexity Analysis and O() 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

目錄大綱(中文翻譯)

目錄

 

I. 人工智慧

1. 簡介

1.1 什麼是人工智慧？

1.2 人工智慧的基礎

1.3 人工智慧的歷史

1.4 最新技術

1.5 總結、參考文獻和歷史註解、練習題

2. 智能代理

2.1 代理和環境

2.2 良好行為：理性的概念

2.3 環境的性質

2.4 代理的結構

2.5 總結、參考文獻和歷史註解、練習題

II. 問題解決

3. 通過搜索解決問題

3.1 解決問題的代理

3.2 示例問題

3.3 尋找解決方案

3.4 無信息搜索策略

3.5 有信息（啟發式）搜索策略

3.6 啟發函數

3.7 總結、參考文獻和歷史註解、練習題

4. 超越傳統搜索

4.1 在優化問題中的局部搜索算法

4.2 在連續空間中的局部搜索

4.3 帶有非確定性動作的搜索

4.4 帶有部分觀察的搜索

4.5 在線搜索代理和未知環境

4.6 總結、參考文獻和歷史註解、練習題

5. 對抗性搜索

5.1 遊戲

5.2 遊戲中的最優決策

5.3 Alpha-Beta剪枝

5.4 不完美的實時決策

5.5 隨機遊戲

5.6 部分可觀察遊戲

5.7 最先進的遊戲程序

5.8 替代方法

5.9 總結、參考文獻和歷史註解、練習題

6. 約束滿足問題

6.1 定義約束滿足問題

6.2 約束傳播：CSP中的推理

6.3 CSP的回溯搜索

6.4 CSP的局部搜索

6.5 問題的結構

6.6 總結、參考文獻和歷史註解、練習題

III. 知識、推理和規劃

7. 邏輯代理

7.1 基於知識的代理

7.2 Wumpus世界

7.3 邏輯

7.4 命題邏輯：一種非常簡單的邏輯

7.5 命題定理證明

7.6 有效的命題模型檢查

7.7 基於命題邏輯的代理

7.8 總結、參考文獻和歷史註解、練習題

8. 一階邏輯

8.1 重新思考表示

8.2 一階邏輯的語法和語義

8.3 使用一階邏輯

8.4 一階邏輯中的知識工程

8.5 總結、參考文獻和歷史註解、練習題

9. 一階邏輯推理

9.1 命題邏輯與一階邏輯推理

9.2 合一和提升

9.3 正向鏈接

9.4 反向鏈接

9.5 解析

9.6 總結、參考文獻和歷史註解、練習題

10. 經典規劃

10.1 經典規劃的定義

10.2 作為狀態空間搜索的規劃算法

10.3 規劃圖

10.4 其他經典規劃方法

10.5 規劃方法的分析

10.6 總結、參考文獻和歷史註解、練習題

11. 在現實世界中的規劃和行動

11.1 時間、日程和資源

11.2 階層規劃

11.3 在不確定領域中的規劃和行動

11.4 多智能體規劃

11.5 總結、參考文獻和歷史註解、練習題

12. 知識表示

12.1

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