了得網研究生_人工智能：智能繫統指南（英文版·第3版）（已被國際多所大學采

內容簡介

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人工智能經常被人們認為是計算機科學中一門高度復雜甚至令人生畏的學科。長期以來人工智能方面的書籍往往包含復雜矩陣代數和微分方程。本書基於作者多年來給沒有多少微積分知識的學生授課時所用的講義。假定讀者沒有編程經驗，以簡單易懂的方式介紹了智能繫統的基礎知識。
尼格尼維斯基編著的《人工智能》目前已經被國際上多所大學（例如，德國的馬格德堡大學、日本的廣島大學、美國的波士頓大學和羅切斯特理工學院等）采納為教材。
如果您正在尋找關於人工智能或智能繫統設計課程的淺顯易懂的入門級教材，如果您不是計算機科學領域的專業人員而又正在尋找介紹基於知識繫統*技術發展的自學指南，本書將是您的*選擇。
與上一版相比，本版進行了全面更新，以反映人工智能領域的*進展。其中新增了數據挖掘與知識發現一章和自組織神經網絡聚類一節內容。同時補充了4個新的案例研究。

作者簡介

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澳大利亞塔斯馬尼亞大學電氣工程和計算機科學繫教授。他的許多研究課題都涉及人工智能和軟計算。他一直致力於電氣工程、過程控制和環境工程中智能繫統的開發和應用，發表了300多篇論文，著有2本專著，並獲得了4項發明專利。

PrefacePreface to the third editionOverview of the bookAcknowledgements1 Introduction to knowledge-based intelligent systems 1.1 Intelligent machines, or what machines can do 1.2 The history of artificial intelligence, or from the DarkAges to knowledge-based systems 1.3 Summary Questions for review References2 Rule-based expert systems 2.1 Introduction, or what is knowledge? 2.2 Rules as a knowledge representation technique 2.3 The main players in the expert system development team 2.4 Structure of a rule-based expert system 2.5 Fundamental characteristics of an expert system 2.6 Forward chaining and backward chaining inference techniques 2.7 MEDIA ADVISOR: a demonstration rule-based expert system 2.8 Conflict resolution 2.9 Advantages and disadvantages of rule-based expert systems 2.10 Summary Questions for review References3 Uncertainty management in rule-based expert systems 3.1 Introduction, or what is uncertainty? 3.2 Basic probability theory 3.3 Bayesian reasoning 3.4 FORECAST: Bayesian accumulation of evidence 3.5 Bias of the Bayesian method 3.6 Certainty factors theory and evidential reasoning 3.7 FORECAST: an application of certainty factors 3.8 Comparison of Bayesian reasoning and certainty factors 3.9 Summary Questions for review References4 Fuzzy expert systems 4.1 Introduction, or what is fuzzy thinking? 4.2 Fuzzy sets 4.3 Linguistic variables and hedges 4.4 Operations of fuzzy sets 4.5 Fuzzy rules 4.6 Fuzzy inference 4.7 Building a fuzzy expert system 4.8 Summary Questions for review References Bibliography5 Frame-based expert systems 5.1 Introduction, or what is a frame? 5.2 Frames as a knowledge representation technique 5.3 Inheritance in frame-based systems 5.4 Methods and demons 5.5 Interaction of frames and rules 5.6 Buy Smart: a frame-based expert system 5.7 Summary Questions for review References Bibliography6 Artificial neural networks 6.1 Introduction, or how the brain works 6.2 The neuron as a simple computing element 6.3 The perceptron 6.4 Multilayer neural networks 6.5 Accelerated learning in multilayer neural networks 6.6 The Hopfield network 6.7 Bidirectional associative memory 6.8 Self-organising neural networks 6.9 Summary Questions for review References7 Evolutionary computation 7.1 Introduction, or can evolution be intelligent? 7.2 Simulation of natural evolution 7.3 Genetic algorithms 7.4 Why genetic algorithms work 7.5 Case study: maintenance scheduling with genetic algorithms 7.6 Evolution strategies 7.7 Genetic programming 7.8 Summary Questions for review References Bibliography8 Hybrid intelligent systems 8.1 Introduction, or how to combine German mechanics withItalian love 8.2 Neural expert systems 8.3 Neuro-fuzzy systems 8.4 ANFIS: Adaptive Neuro-Fuzzy Inference System 8.5 Evolutionary neural networks 8.6 Fuzzy evolutionary systems 8.7 Summary Questions for review References9 Knowledge engineering 9.1 Introduction, or what is knowledge engineering? 9.2 Will an expert system work for my problem? 9.3 Will a fuzzy expert system work for my problem? 9.4 Will a neural network work for my problem? 9.5 Will genetic algorithms work for my problem? 9.6 Will a hybrid intelligent system work for my problem? 9.7 Summary Questions for review References10 Data mining and knowledge discovery 10.1 Introduction, or what is data mining? 10.2 Statistical methods and data visualisation 10.3 Principal component analysis 10.4 Relational databases and database queries 10.5 The data warehouse and multidimensional data analysis 10.6 Decision trees 10.7 Association rules and market basket analysis 10.8 Summary Questions for review ReferencesGlossaryAppendix: AI tools and vendorsindex


Preface
Preface to the third edition
Overview of the book
Acknowledgements
1  Introduction to knowledge-based intelligent systems
  1.1  Intelligent machines, or what machines can do
  1.2  The history of artificial intelligence, or from the Dark
Ages to knowledge-based systems
  1.3  Summary
  Questions for review
  References
2  Rule-based expert systems
  2.1  Introduction, or what is knowledge?
  2.2  Rules as a knowledge representation technique
  2.3  The main players in the expert system development team
  2.4  Structure of a rule-based expert system
  2.5  Fundamental characteristics of an expert system
  2.6  Forward chaining and backward chaining inference techniques
  2.7  MEDIA ADVISOR: a demonstration rule-based expert system
  2.8  Conflict resolution
  2.9  Advantages and disadvantages of rule-based expert systems
  2.10 Summary
  Questions for review
  References
3  Uncertainty management in rule-based expert systems
  3.1  Introduction, or what is uncertainty?
  3.2  Basic probability theory
  3.3  Bayesian reasoning
  3.4  FORECAST: Bayesian accumulation of evidence
  3.5  Bias of the Bayesian method
  3.6  Certainty factors theory and evidential reasoning
  3.7  FORECAST: an application of certainty factors
  3.8  Comparison of Bayesian reasoning and certainty factors
  3.9  Summary
  Questions for review
  References
4  Fuzzy expert systems
  4.1  Introduction, or what is fuzzy thinking?
  4.2  Fuzzy sets
  4.3  Linguistic variables and hedges
  4.4  Operations of fuzzy sets
  4.5  Fuzzy rules
  4.6  Fuzzy inference
  4.7  Building a fuzzy expert system
  4.8  Summary
  Questions for review
  References
  Bibliography
5   Frame-based expert systems
  5.1  Introduction, or what is a frame?
  5.2  Frames as a knowledge representation technique
  5.3  Inheritance in frame-based systems
  5.4  Methods and demons
  5.5  Interaction of frames and rules
  5.6  Buy Smart: a frame-based expert system
  5.7  Summary
  Questions for review
  References
  Bibliography
6  Artificial neural networks
  6.1  Introduction, or how the brain works
  6.2  The neuron as a simple computing element
  6.3  The perceptron
  6.4  Multilayer neural networks
  6.5  Accelerated learning in multilayer neural networks
  6.6  The Hopfield network
  6.7  Bidirectional associative memory
  6.8  Self-organising neural networks
  6.9  Summary
  Questions for review
  References
7  Evolutionary computation
  7.1  Introduction, or can evolution be intelligent?
  7.2  Simulation of natural evolution
  7.3  Genetic algorithms
  7.4  Why genetic algorithms work
  7.5  Case study: maintenance scheduling with genetic algorithms
  7.6  Evolution strategies
  7.7  Genetic programming
  7.8  Summary
  Questions for review
  References
  Bibliography
8  Hybrid intelligent systems
  8.1  Introduction, or how to combine German mechanics with
Italian love
  8.2  Neural expert systems
  8.3  Neuro-fuzzy systems
  8.4  ANFIS: Adaptive Neuro-Fuzzy Inference System
  8.5  Evolutionary neural networks
  8.6  Fuzzy evolutionary systems
  8.7  Summary
  Questions for review
  References
9  Knowledge engineering
  9.1  Introduction, or what is knowledge engineering?
  9.2  Will an expert system work for my problem?
  9.3  Will a fuzzy expert system work for my problem?
  9.4  Will a neural network work for my problem?
  9.5  Will genetic algorithms work for my problem?
  9.6  Will a hybrid intelligent system work for my problem?
  9.7  Summary
  Questions for review
  References
10  Data mining and knowledge discovery
  10.1  Introduction, or what is data mining?
  10.2  Statistical methods and data visualisation
  10.3  Principal component analysis
  10.4  Relational databases and database queries
  10.5  The data warehouse and multidimensional data analysis
  10.6  Decision trees
  10.7  Association rules and market basket analysis
  10.8  Summary
  Questions for review
  References
Glossary
Appendix: AI tools and vendors
index

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