本书基于作者多年来给没有多少微积分知识的学生授课时所用的讲义，介绍了人工智能或智能系统设计方面的基本知识，主要内容包括：基于规则的专家系统、模糊专家系统、基于框架的专家系统、人工神经网络、进化计算、混合智能系统、知识工程、数据挖掘等。

人工智能经常被人们认为是计算机科学中一门高度复杂甚至令人生畏的学科。长期以来人工智能方面的书籍往往包含复杂矩阵代数和微分方程。本书基于作者多年来给没有多少微积分知识的学生授课时所用的讲义，假定读者没有编程经验，以简单易懂的方式介绍了智能系统的基础知识。
本书目前已经被国际上多所大学（例如，德国的马格德堡大学、日本的广岛大学、美国的波士顿大学和罗切斯特理工学院等）采纳为教材。
如果您正在寻找关于人工智能或智能系统设计课程的浅显易懂的入门级教材，如果您不是计算机科学领域的专业人员而又正在寻找介绍基于知识系统最新技术发展的自学指南，本书将是您的最佳选择。
与上一版相比，本版进行了全面更新，以反映人工智能领域的最新进展。其中新增了数据挖掘与知识发现一章和自组织神经网络聚类一节内容，同时补充了4个新的案例研究。
Michael Negnevitsky 澳大利亚塔斯马尼亚大学电气工程和计算机科学系教授。他的许多研究课题都涉及人工智能和软计算。他一直致力于电气工程、过程控制和环境工程中智能系统的开发和应用，发表了300多篇论文，著有2本专著，并获得了4项发明专利。

计算机\人工智能

人工智能经常被人们认为是计算机科学中一门高度复杂甚至令人生畏的学科。长期以来人工智能方面的书籍往往包含复杂矩阵代数和微分方程。本书基于作者多年来给没有多少微积分知识的学生授课时所用的讲义，假定读者没有编程经验，以简单易懂的方式介绍了智能系统的基础知识。
本书目前已经被国际上多所大学（例如，德国的马格德堡大学、日本的广岛大学、美国的波士顿大学和罗切斯特理工学院等）采纳为教材。
如果您正在寻找关于人工智能或智能系统设计课程的浅显易懂的入门级教材，如果您不是计算机科学领域的专业人员而又正在寻找介绍基于知识系统最新技术发展的自学指南，本书将是您的最佳选择。
与上一版相比，本版进行了全面更新，以反映人工智能领域的最新进展。其中新增了数据挖掘与知识发现一章和自组织神经网络聚类一节内容，同时补充了4个新的案例研究。

（澳）Michael Negnevitsky 著：Michael Negnevitsky 澳大利亚塔斯马尼亚大学电气工程和计算机科学系教授。他的许多研究课题都涉及人工智能和软计算。他一直致力于电气工程、过程控制和环境工程中智能系统的开发和应用，发表了300多篇论文，著有2本专著，并获得了4项发明专利。

Preface 　v
Preface to the third edition vii
Overview of the book viii
Acknowledgements xi
1 Introduction to knowledge-based intelligent systems 1
1.1 Intelligent machines, or what machines can do 1
1.2 The history of artificial intelligence, or from the ‘Dark Ages’to knowledge-based systems 4
1.3 Summary 17
Questions for review 21
References 22
2 Rule-based expert systems 25
2.1 Introduction, or what is knowledge 25
2.2 Rules as a knowledge representation technique 26
2.3 The main players in the expert system development team 28
2.4 Structure of a rule-based expert system 30
2.5 Fundamental characteristics of an expert system 33
2.6 Forward chaining and backward chaining inference techniques 35
2.7 MEDIA ADVISOR: a demonstration rule-based expert system 41
2.8 Conflict resolution 47
2.9 Advantages and disadvantages of rule-based expert systems 50
2.10　　Summary 51
Questions for review 53
References 54
3 Uncertainty management in rule-based expert systems 55
3.1 Introduction, or what is uncertainty 55
3.2 Basic probability theory 57
3.3 Bayesian reasoning 61
3.4 FORECAST: Bayesian accumulation of evidence 65
3.5 Bias of the Bayesian method 72
3.6 Certainty factors theory and evidential reasoning 74
3.7 FORECAST: an application of certainty factors 80
3.8 Comparison of Bayesian reasoning and certainty factors 82
3.9 Summary 83
Questions for review 85
References 85
4 Fuzzy expert systems 87
4.1 Introduction, or what is fuzzy thinking 87
4.2 Fuzzy sets 89
4.3 Linguistic variables and hedges 94
4.4 Operations of fuzzy sets 97
4.5 Fuzzy rules 103
4.6 Fuzzy inference 106
4.7 Building a fuzzy expert system 113
4.8 Summary 125
Questions for review 126
References 127
Bibliography 127
5 Frame-based expert systems 131
5.1 Introduction, or what is a frame 131
5.2 Frames as a knowledge representation technique 133
5.3 Inheritance in frame-based systems 138
5.4 Methods and demons 142
5.5 Interaction of frames and rules 146
5.6 Buy Smart: a frame-based expert system 149
5.7 Summary 161
Questions for review 163
References 163
Bibliography 164
6 Artificial neural networks 165
6.1 Introduction, or how the brain works 165
6.2 The neuron as a simple computing element 168
6.3 The perceptron 170
6.4 Multilayer neural networks 175
6.5 Accelerated learning in multilayer neural networks 185
6.6 The Hopfield network 188
6.7 Bidirectional associative memory 196
6.8 Self-organising neural networks 200
6.9 Summary 212
Questions for review 215
References 216
7 Evolutionary computation 219
7.1 Introduction, or can evolution be intelligent 219
7.2 Simulation of natural evolution 219
7.3 Genetic algorithms 222
7.4 Why genetic algorithms work 232
7.5 Case study: maintenance scheduling with genetic algorithms 235
7.6 Evolution strategies 242
7.7 Genetic programming 245
7.8 Summary 254
Questions for review 255
References 256
Bibliography 257
8 Hybrid intelligent systems 259
8.1 Introduction, or how to combine German mechanics with Italian love 259
8.2 Neural expert systems 261
8.3 Neuro-fuzzy systems 268
8.4 ANFIS: Adaptive Neuro-Fuzzy Inference System 277
8.5 Evolutionary neural networks 285
8.6 Fuzzy evolutionary systems 290
8.7 Summary 296
Questions for review 297
References 298
9 Knowledge engineering 301
9.1 Introduction, or what is knowledge engineering 301
9.2 Will an expert system work for my problem 308
9.3 Will a fuzzy expert system work for my problem 317
9.4 Will a neural network work for my problem 323
9.5 Will genetic algorithms work for my problem 343
9.6 Will a hybrid intelligent system work for my problem 348
9.7 Summary 357
Questions for review 359
References 362
10　　Data mining and knowledge discovery 365
10.1　　Introduction, or what is data mining 365
10.2　　Statistical methods and data visualisation 369
10.3　　Principal component analysis 374
10.4　　Relational databases and database queries 386
10.5　　The data warehouse and multidimensional data analysis 391
10.6　　Decision trees 401
10.7　　Association rules and market basket analysis 410
10.8　　Summary 418
Questions for review 420
References 421
Glossary 425
Appendix: AI tools and vendors 451
Index 471