“在该领域里学生经常遇到许多很难的概念；通过深刻的实例与简单明了的视图，该书清晰而准确地阐述了这些概念。”
　　——Joseph Lewis，圣迭戈州立大学
　　
　　“本书是人工智能课程的完美补充。它既给读者以历史的观点，又给出所有技术的实用指南。这是一本必须要推荐的人工智能的图书。”
　　——Pascal Rebreyend，瑞典达拉那大学
　　
　　“该书的写作风格和全面的论述使它成为人工智能领域很有价值的文献。”
　　——Malachy Eaton，利默里克大学
　　本版新增内容
　　
　　新增一章，介绍用于机器学习的随机方法，包括一阶贝叶斯网络、各种隐马尔可夫模型、马尔可夫随机域推理和循环信念传播。
　　介绍针对期望最大化学习以及利用马尔可夫链蒙特卡罗采样的结构化学习的参数选择，加强学习中马尔可夫决策过程的利用。
　　介绍智能体技术和本体的使用。
　　介绍自然语言处理的动态规划（Earley语法分析器）以及Viterbi等其他概率语法分析技术。
　　书中的许多算法采用Prolog、Lisp和Java语言来构建。
　　作者简介
　　George F. Luger　1973年在宾夕法尼亚大学获得博士学位，并在之后的5年间在爱丁堡大学人工智能系进行博士后研究，现在是新墨西哥大学计算机科学研究、语言学及心理学教授。

I was very pleased to be asked to produce the sixth edition of my artificial intelligencebook. It is a compliment to the earlier editions， started over twenty years ago, that ourapproach to AI has been so highly valued. It is also exciting that, as new development inthe field emerges, we are able to present much of it in each new edition. We thank ourmany readers, colleagues, and students for keeping our topics relevant and our presenta-tion up to date.
　　Many sections of the earlier editions have endured remarkably well, including thepresentation of logic, search algorithms, knowledge representation, production systems,machine learning, and, in the supplementary materials, the programming techniquesdeveloped in Lisp, Prolog, and with this edition, Java. These remain central to the practiceof artificial intelligence, and a constant in this new edition.
　　This book remains accessible. We introduce key representation techniques includinglogic, semantic and connectionist networks, graphical models, and many more. Our searchalgorithms are presented clearly, first in pseudocode, and then in the supplementary mate-rials, many of them are implemented in Prolog, Lisp, and/or Java. It is expected that themotivated students can take our core implementations and extend them to new excitingapplications.
　　We created, for the sixth edition, a new machine learning chapter based on stochasticmethods （Chapter 13）. We feel that the stochastic technology is having an increasinglylarger impact on AI, especially in areas such as diagnostic and prognostic reasoning, natu-ral language analysis, robotics, and machine learning.

“在该领域里学生经常遇到许多很难的概念；通过深刻的实例与简单明了的视图，该书清晰而准确地阐述了这些概念。”
　　——Joseph Lewis，圣迭戈州立大学
　　
　　“本书是人工智能课程的完美补充。它既给读者以历史的观点，又给出所有技术的实用指南。这是一本必须要推荐的人工智能的图书。”
　　——Pascal Rebreyend，瑞典达拉那大学
　　
　　“该书的写作风格和全面的论述使它成为人工智能领域很有价值的文献。”
　　——Malachy Eaton，利默里克大学
　　本版新增内容
　　
　　新增一章，介绍用于机器学习的随机方法，包括一阶贝叶斯网络、各种隐马尔可夫模型、马尔可夫随机域推理和循环信念传播。
　　介绍针对期望最大化学习以及利用马尔可夫链蒙特卡罗采样的结构化学习的参数选择，加强学习中马尔可夫决策过程的利用。
　　介绍智能体技术和本体的使用。
　　介绍自然语言处理的动态规划（Earley语法分析器）以及Viterbi等其他概率语法分析技术。
　　书中的许多算法采用Prolog、Lisp和Java语言来构建。
　　作者简介
　　George F. Luger　1973年在宾夕法尼亚大学获得博士学位，并在之后的5年间在爱丁堡大学人工智能系进行博士后研究，现在是新墨西哥大学计算机科学研究、语言学及心理学教授。

George F. Luger：George F. Luger: 1973年在宾夕法尼亚大学获得博士学位。在其后的五年，他在爱丁堡大学人工智能系从事博士后研究工作。目前，他是新墨西哥大学的计算机科学、语言学以及心理学教授。他的研究兴趣、课程信息异己发表的论文可从以下网址找到：http://www.cs.unm.edu/~luger/

Preface
Publishers Acknowledgements
PART Ⅰ ARTIFIClAL INTELLIGENCE：ITS ROOTS AND SCOPE
1 A1：HISTORY AND APPLICATIONS
1.1 From Eden to ENIAC：Attitudes toward Intelligence，Knowledge，andHuman Artifice
1.2 0verview ofAl Application Areas
1.3 Artificial Intelligence A Summary
1.4 Epilogue and References
1.5 Exercises

PART Ⅱ ARTIFlClAL INTELLIGENCE AS REPRESENTATION AN D SEARCH
2 THE PREDICATE CALCULUS
2.0 Intr0血ction
2.1 The Propositional Calculus
2.2 The Predicate Calculus
2.3 Using Inference Rules to Produce Predicate Calculus Expressions
2.4 Application：A Logic-Based Financial Advisor
2.5 Epilogue and References
2.6 Exercises

3 STRUCTURES AND STRATEGIES FOR STATE SPACE SEARCH
3.0 Introducfion
3.1 GraphTheory
3.2 Strategies for State Space Search
3.3 using the state Space to Represent Reasoning with the Predicate Calculus
3.4 Epilogue and References
3.5 Exercises

4 HEURISTIC SEARCH
4.0 Introduction
4.l Hill Climbing and Dynamic Programmin9
4.2 The Best-First Search Algorithm
4.3 Admissibility,Monotonicity,and Informedness
4.4 Using Heuristics in Games
4.5 Complexity Issues
4.6 Epilogue and References
4.7 Exercises

5 STOCHASTIC METHODS
5.0 Introduction
5.1 The Elements ofCountin9
5.2 Elements ofProbabilityTheory
5.3 Applications ofthe Stochastic Methodology
5.4 BayesTheorem
5.5 Epilogue and References
5.6 Exercises

6 coNTROL AND IMPLEMENTATION OF STATE SPACE SEARCH
6.0 Introduction l93
6.1 Recursion.Based Search
6.2 Production Systems
6.3 The Blackboard Architecture for Problem Solvin9
6.4 Epilogue and References
6.5 Exercises

PARTⅢ CAPTURING INTELLIGENCE：THE AI CHALLENGE
7 KNOWLEDGE REPRESENTATION
7.0 Issues in Knowledge Representation
7.1 A BriefHistory ofAI Representational Systems
7.2 Conceptual Graphs：A Network Language
7.3 Alternative Representations and Ontologies
7.4 Agent Based and Distributed Problem Solving
7.5 Epilogue and References
7.6 Exercises

8 STRONG METHOD PROBLEM SOLVING
8.0 Introduction
8.1 Overview ofExpert Sygem Technology
8.2 Rule.Based Expert Sygems
8.3 Model-Based,Case Based and Hybrid Systems
8.4 Planning
8.5 Epilogue and References
8.6 Exercises
9 REASONING IN UNCERTAIN STUATIONS
9.0 Introduction
9.1 Logic-Based Abductive Inference
9.2 Abduction：Alternatives to Logic
9.3 The Stochastic Approach to Uncertainty
9.4 Epilogue and References
9.5 Exercises

PART Ⅳ
MACHINE LEARNING
10 MACHINE LEARNING：SYMBOL-BASED
10.0 Introduction
10.1 A Framework for Symbol based Learning
10.2 version Space Search
10.3 The ID3 Decision Tree Induction Algorithm
10.4 Inductive Bias and Learnability
10.5 Knowledge and Learning
10.6 Unsupervised Learning
10.7 Reinforcement Learning
10.8 Epilogue and Referenees
10.9 Exercises

11 MACHINE LEARNING：CONNECTIONtST
11.0 Introduction
11.1 Foundations for Connectionist Networks
11.2 Perceptron Learning
11.3 Backpropagation Learning
11.4 Competitive Learning
11.5 Hebbian Coincidence Learning
11.6 Attractor Networks or“Memories”
11.7 Epilogue and References
11.8 Exercises 506

12 MACHINE LEARNING：GENETIC AND EMERGENT
12.0 Genetic and Emergent MedeIs ofLearning
12.1 11Ic Genetic Algorithm
12.2 Classifier Systems and Genetic Programming
12.3 Artmcial Life and Society-Based Learning
12.4 EpilogueandReferences
12.5 Exercises

13 MACHINE LEARNING：PROBABILISTIC
13.0 Stochastic andDynamicModelsofLearning
13.1 Hidden Markov Models(HMMs)
13.2 DynamicBayesianNetworksandLearning
13.3 Stochastic Extensions to Reinforcement Learning
13.4 EpilogueandReferences
13.5 Exercises

PART Ⅴ
AD，ANCED TOPlCS FOR Al PROBLEM SOLVING
14 AUTOMATED REASONING
14.0 Introduction to Weak Methods inTheorem Proving
14.1 TIIeGeneralProblem SolverandDifiel"enceTables
14.2 Resolution TheOrem Proving
14.3 PROLOG and Automated Reasoning
14.4 Further Issues in Automated Reasoning
14.5 EpilogueandReferences
14.6 Exercises

15 UNDERs-rANDING NATURAL LANGUAGE
15.0 TheNaturalLang~~geUnderstandingProblem
15.1 Deconstructing Language：An Analysis
15.2 Syntax
15.3 TransitionNetworkParsers and Semantics
15.4 StochasticTools forLanguage Understanding
15.5 Natural LanguageApplications
15.6 Epilogue and References
15.7 Exercises
……
PART Ⅵ EPILOGUE
16 ARTIFICIAL INTELLIGENCE AS EMPIRICAL ENQUIRY