Artificial Intelligence: A Modern Approach, 3/e (GE-Paperback)
暫譯: 人工智慧：現代方法（第三版）
Stuart Russell , Peter Norvig
- 出版商: Pearson FT Press
- 出版日期: 2016-05-01
- 定價: $1,450
- 售價: 9.8 折 $1,421
- 語言: 英文
- 頁數: 1152
- ISBN: 1292153962
- ISBN-13: 9781292153964
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相關分類:
人工智慧
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其他版本:
Artificial Intelligence: A Modern Approach, 4/e (美國原版)
Artificial Intelligence: A Modern Approach, 4/e (IE-Paperback)
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相關主題
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.
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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.
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- The Internet as a sample application for intelligent systems — Examples of logical reasoning, planning, and natural language processing using Internet agents.
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Promotes student interest with interesting, relevant exercises.
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- 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.
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Brings students up to date on the latest technologies, and presents concepts in a more unified manner.
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- Updated and expanded exercises — 30% of the exercises are revised or NEW.
- More Online Software.
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Allows many more opportunities for student projects on the web.
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- A unified, agent-based approach to AI — Organizes the material around the task of building intelligent agents.
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Shows students how the various subfields of AI fit together to build actual, useful programs.
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- Comprehensive, up-to-date coverage — Includes a unified view of the field organized around the rational decision making paradigm.
- A flexible format.
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Makes the text adaptable for varying instructors' preferences.
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- In-depth coverage of basic and advanced topics.
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Provides students with a basic understanding of the frontiers of AI without compromising complexity and depth.
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- 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.
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Gives instructors and students a choice of projects; reading and running the code increases understanding.
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Author Maintained Website
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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!
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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)
### 內容描述
本書適用於一或兩學期的本科或研究生人工智慧課程。
這本暢銷書的期待已久的修訂版提供了最全面、最新的人工智慧理論與實踐介紹。
### 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幫助增進理解，使本書對更廣泛的學生群體可及。
- **網際網路作為智能系統的範例應用** — 使用網際網路代理的邏輯推理、規劃和自然語言處理的範例。
- 透過有趣且相關的練習來促進學生的興趣。
- **增加的材料涵蓋範圍** — 新增或擴展了約束滿足、局部搜索規劃方法、多代理系統、博弈論、統計自然語言處理及隨時間的不確定推理的內容。對於概率推理、快速命題推理、包括EM的概率學習方法及其他主題的算法進行了更詳細的描述。
- 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%的練習已被修訂或為全新內容。
- **更多的線上軟體。**
- 提供更多的機會讓學生在網路上進行專案。
- **統一的基於代理的人工智慧方法** — 將材料組織在構建智能代理的任務周圍。
- 向學生展示人工智慧的各個子領域如何協同工作以構建實際的、有用的程式。
- **全面、最新的涵蓋範圍** — 包含圍繞理性決策範式組織的統一視角。
- **靈活的格式。**
- 使文本能夠適應不同講師的偏好。
- **深入涵蓋基本和進階主題。**
- 提供學生對人工智慧前沿的基本理解，而不妥協於複雜性和深度。
- **主要人工智慧算法的伪代码版本**以統一的方式呈現，並且**實際的 Common Lisp 和 Python**實現可透過網際網路獲得。
- 為講師和學生提供專案選擇；閱讀和運行代碼能增進理解。
- **作者維護的網站**
- 訪問 [http://aima.cs.berkeley.edu/](http://aima.cs.berkeley.edu/) 獲取與文本相關的**評論和討論**、**網路上的人工智慧資源**、**線上代碼庫**、**講師資源**等！
### 本版新內容
本版捕捉了自2003年上版以來人工智慧領域的變化。人工智慧技術的重要應用包括實用語音識別、機器翻譯、自動駕駛車輛和家用機器人等的廣泛部署。還有算法的里程碑，例如跳棋遊戲的解決方案。此外，在概率推理、機器學習和計算機視覺等領域也取得了大量理論進展。從作者的角度來看，最重要的是我們對該領域的思考方式的持續演變，因此本書的組織方式也隨之改變。主要變更如下：
- 更加強調部分可觀察和非確定性環境，特別是在搜索和規劃的非概率設置中。在這些設置中引入了**信念狀態**（一組可能的世界）和**狀態估計**（維持信念狀態）的概念；在書的後面部分，則加入了概率的內容。
- 除了討論環境類型和代理類型外，還更深入地涵蓋了代理可以使用的**表示法**類型。區分了**原子**表示法（每個世界狀態被視為黑箱）、**分解**表示法（狀態是一組屬性/值對）和**結構化**表示法（世界由物體及其之間的關係組成）之間的差異。
- 規劃的涵蓋範圍更深入，探討了在部分可觀察環境中的應急規劃，並包括了一種新的分層規劃方法。
- 新增了有關一階概率模型的材料，包括在不確定存在哪些物體的情況下的**開放宇宙**模型。
- 介紹機器學習的章節完全重寫，強調更廣泛的現代學習算法，並將其置於更堅實的理論基礎上。
- 擴展了對網路搜索和信息提取的涵蓋，以及從非常大數據集學習的技術。
- 本版中20%的引用文獻是2003年以後發表的作品。
- 約20%的材料是全新的。其餘80%反映了舊有的工作，但大部分已重寫，以呈現該領域的更統一的圖景。
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)
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
