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　 本书介绍了人工智能领域中重要的一个概念——智能代理。本书从基本的反应式代理入手，逐步向人们展示了现代人工智能不断增强的认知能力，同时也例证了该领域中重要且经久不衰的思维、思想。神经网络、遗传程序设计、计算机视觉、探视搜索、知识表示和推理、贝叶斯网络、规划和语言理解等有关人工智能的重要内容都通过本书所描述的各种代理的不断增长的能力得以展现。本书作者是人工智能领域的主要开创者和重要带头人，正是他提供给了广大读者一个耳目一新和富有生机的合成技术，该技术将领导人类把整个人工智能领域的研究引向一个新的境界。 Nils J. Nilsson: Artificial Intelligence, A New Synthesis. Copyright @ 1998 by Morgan Kaufmann Publishers, Inc. Harcourt Asia Pte Ltd under special arrangement with Morgan Kaufmann authorizes China Machine Press to print and exclusively distribute this edition, which is the only authorized complete and unabridged reproduction of the latest American Edition published and priced for sale in China only, not including Hong Kong SAR and Taiwan. Unauthorized export of this edition is a violation of the Copyright Act. Violation of this Law is subjected to Civil and Criminal penalties.

Preface

1 Introduction

1.1 What is AI?

1.2 Approaches to Artificial Intelligence

1.3 Brief History of AI

1.4 Plan of the Book

1.5 Additional Readings and Discussion

I Reactive Machines

2 Stimulus-Response Agents

2.1 Perception and Action

2.2 Representing and Implementing Action Functions

2.3 Additional Readings and Discussion

3 Neural Networks

3.1 Introduction

3.2 Training Single TLUs

3.3 Neural Networks

3.4 Generalization, Accuracy, and Overfitting

3.5 Additional Readings and Discussion

4 Machine Evolution

4.1 Evolutionary Computation

4.2 Genetic Programming

4.3 Additional Readings and Discussion

5 State Machines

5.1 Representing the Environment by Feature Vectors

5.2 Elman Networks

5.3 Iconic Representations

5.4 Blackboard Systems

5.5 Additional Readings and Discussion

6 Robot Vision

6.1 Introduction

6.2 Steering a Van

6.3 Two Stages of Robot Vision

6.4 Image Processing

6.5 Scene Analysis

6.6 Stereo Vision

6.7 Additional Readings and Discussion

II Search in State Spaces

7 Agents that Plan

7.1 Memory Versus Computation

7.2 State-Space Graphs

7.3 Searching Explicit State Spaces

7.4 Feature-Based State Spaces

7.5 Graph Notation 7.6 Additional Readings and Discussion

8 Uninformed Search

8.1 Formulating the State Space

8.2 Components of Implicit State-Space Graphs

8.3 Breadth-First Search

8.4 Depth-First or Bracktracking Search

8.5 Iterative Deepening

8.6 Additional Readings and Discussion

9 Heuristic Search

9.1 Using Evaluation Functions

9.2 A General Graph-Searching Algorithm

9.3 Heuristic Functions and Search Efficiency

9.4 Additional Readings and Discussion

10 Planning, Acting, and Learning

10.1 The Sense/Plan/Act Cycle

10.2 Approximate Search

10.3 Learning Heuristic Functions

10.4 Rewards Instead of Goals

10.5 Additional Readings and Discussion

11 Alternative Search Formulations and Applications

11.1 Assignment Problems

11.2 Constructive Methods

11.3 Heuristic Repair

11.4 Function Optimization

12 Adversarial Search

12.1 Two-Agent Games

12.2 The Minimax Procedure

12.3 The Alpha-Beta Procedure

12.4 The Search Efficiency of the Alpha-Beta Procedure

12.5 Other Important Matters

12.6 Games of Chance

12.7 Learning Evaluation Functions

12.8 Additional Readings and Discussion

III Knowledge Representation and Reasoning

13 The Propositional Calculus

13.1 Using Constraints on Feature Values

13.2 The Language

13.3 Rules of Inference

13.4 Definition of Proof

13.5 Semantics

13.6 Soundness and Completeness

13.7 The PSAT Problem

13.8 Other Important Topics

14 Resolution in The Propositional Calculus

14.1 A New Rule of Inference: Resolution

14.2 Converting Arbitrary wffs to Conjunctions of Clauses

14.3 Resolution Refutations

14.4 Resolution Refutation Search Strategies

14.5 Horn Clauses

15 The Predicate Calculus

15.1 Motivation

15.2 The Language and its Syntax

15.3 Semantics

15.4 Quantification

15.5 Semantics of Quantifiers

15.6 Predicate Calculus as a Language for Representing Knowledge

15.7 Additional Readings and Discussion

16 Resolution in the Predicate Calculus

16.1 Unification

16.2 Predicate-Calculus Resolution

16.3 Completeness and Soundness

16.4 Converting Arbitrary wffs to Clause Form

16.5 Using Resolution to Prove Theorems

16.6 Answer Extraction

16.7 The Equality Predicate

16.8 Additional Readings and Discussion

17 Knowledge-Based Systems

17.1 Confronting the Real World

17.2 Reasoning Using Horn Clauses

17.3 Maintenance in Dynamic Knowledge Bases

17.4 Rule-Based Expert Systems

17.5 Rule Learning

17.6 Additional Readings and Discussion

18 Representing Commonsense Knowledge

18.1 The Commonsense World

18.2 Time

18.3 Knowledge Representation by Networks

18.4 Additional Readings and Discussion

19 Reasoning with Uncertain Information

19.1 Review of Probability Theory

19.2 Probabilistic Inference

19.3 Bayes Networks

19.4 Patterns of Inference in Bayes Networks

19.5 Uncertain Evidence

19.6 D-Seperation

19.7 Probabilistic Inference in Polytrees

19.8 Additional Readings and Discussion

20 Learning and Acting with Bayes Nets

20.1 Learning Bayes Nets

20.2 Probabilistic Inference and Action

20.3 Additional Readings and Discussion

IV Planning Method Based on Logic

21 The Situation Calculus

21.1 Reasoning about States and Actions

21.2 Some Difficulties

21.3 Generating Plans

21.4 Additional Reading and Discussion

22 Planning

22.1 STRIPS Planning Systems

22.2 Plan Spaces and Partial-Order Planning

22.3 Hierarchical Planning

22.4 Learning Plans'

22.5 Additional Readings and Discussion

V Communication and Integration

23 Multiple Agents

23.1 Interacting Agents

23.2 Models of Other Agents

23.3 A Modal Logic of Knowledge

23.4 Additional Readings and Discussion

24 Communication Among Agents

24.1 Speech Acts

24.2 Understanding Language Strings

24.3 Efficient Communication

24.4 Natural Language Processing

24.5 Additional Readings and Discussion

25 Agent Architectures

25.1 Three-Level Architectures

25.2 Goal Arbitration

25.3 The Triple-Tower Architecture

25.4 Bootstrapping

25.5 Additional Readings and Discussion

About the author

Nils J. Nilsson's long and rich research career has contributed much to Al. His previous books,

considered classics in the field, include Learning Machines, Problem-Solving Methods in Artificial

Intelligence, Logical Foundations of Artificial Intelligence, and Principles of Artificial Intelligence.

Dr. Nilsson is Kumagai Professor of Engineering, Emeritus, at Stanford University. He has served

on the editorial boards of Artificial Intelligence and Machine Learning and as an area editor for the

Joumal of the Association for Computing Machinery. Former chairman of the Department of

Computer Science at Stanford and Former Director of the SRl Artificial Intelligence Center, he is

also a past president and fellow of the American Association for Artificial Intelligence.

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譬如，表达式 $overline{x_1} overline{x_2} overline{x_3} overline{x_4} + x_1overline{x_1}$给出了沿边界运动的机器人应向北移动的情况。

我们很容易证明$n$个变量可组成$3^n$种可能的单项式。

大小为$k$或更小的单项式的总数由以下公式约束：

begin{equation}

sum_{i=0}^k C(2n, i) = O(n^k)

end{equation}

其中，$C(2i, j) = frac{i!}{(i-j)!j!}$。

譬如，图2-6显示了由单个TLU对函数

begin{equation}

overline{x_1}overline{x_2}=(s_2+s_3)(overline{S_4+S_5})=(s_2+s_3)overline{S_4}overline{S_5}

end{equation}

的实现过程。

“分类（classifier）系统”是GA的分支之一[Holland 1986]，且[Holland 1975, pp.171以后，第2版。已经成功地用于进化程序这一目的。

与此类似，当且仅当前一段时间步骤中$w_4$值为$1$，且机器人北移，$w_3$值为$1$，依此类推（否则，$w_i$值为$0$）。

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