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E-raamat: Artificial Intelligence: A Guide to Intelligent Systems

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  • Formaat: PDF+DRM
  • Ilmumisaeg: 09-Nov-2011
  • Kirjastus: Addison Wesley
  • Keel: eng
  • ISBN-13: 9781408225752
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Artificial Intelligence: A Guide to Intelligent SystemsSuurem pilt
  • Formaat: PDF+DRM
  • Ilmumisaeg: 09-Nov-2011
  • Kirjastus: Addison Wesley
  • Keel: eng
  • ISBN-13: 9781408225752
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Negnevitsky shows students how to build intelligent systems drawing on techniques from knowledge-based systems, neural networks, fuzzy systems, evolutionary computation and now also intelligent agents. The principles behind these techniques are explained without resorting to complex mathematics, showing how the various techniques are implemented, when they are useful and when they are not. No particular programming language is assumed and the book does not tie itself to any of the software tools available. However, available tools and their uses are described, and program examples are given in Java.

The lack of assumed prior knowledge makes this book ideal for any introductory courses in artificial intelligence or intelligent systems design, while the contemporary coverage means more advanced students will benefit by discovering the latest state-of-the-art techniques, particularly in intelligent agents and knowledge discovery.

Arvustused

This book covers many areas related to my module. I would be happy to recommend this book to my students. I believe my students would be able to follow this book without any difficulty. Book chapters are very well organised and this will help me to pick and choose the subjects related to this module. Dr Ahmad Lotfi, Nottingham Trent University, UK

Preface xi
Preface to the third edition xiii
Overview of the book xv
Acknowledgements xix
1 Introduction to knowledge-based intelligent systems
1(24)
1.1 Intelligent machines, or what machines can do
1(3)
1.2 The history of artificial intelligence, or from the `Dark Ages' to knowledge-based systems
4(13)
1.3 Summary
17(8)
Questions for review
21(1)
References
22(3)
2 Rule-based expert systems
25(30)
2.1 Introduction, or what is knowledge?
25(1)
2.2 Rules as a knowledge representation technique
26(2)
2.3 The main players in the expert system development team
28(2)
2.4 Structure of a rule-based expert system
30(3)
2.5 Fundamental characteristics of an expert system
33(2)
2.6 Forward chaining and backward chaining inference techniques
35(6)
2.7 MEDIA ADVISOR: a demonstration rule-based expert system
41(6)
2.8 Conflict resolution
47(3)
2.9 Advantages and disadvantages of rule-based expert systems
50(1)
2.10 Summary
51(4)
Questions for review
53(1)
References
54(1)
3 Uncertainty management in rule-based expert systems
55(32)
3.1 Introduction, or what is uncertainty?
55(2)
3.2 Basic probability theory
57(4)
3.3 Bayesian reasoning
61(4)
3.4 FORECAST: Bayesian accumulation of evidence
65(7)
3.5 Bias of the Bayesian method
72(2)
3.6 Certainty factors theory and evidential reasoning
74(6)
3.7 FORECAST: an application of certainty factors
80(2)
3.8 Comparison of Bayesian reasoning and certainty factors
82(1)
3.9 Summary
83(4)
Questions for review
85(1)
References
85(2)
4 Fuzzy expert systems
87(44)
4.1 Introduction, or what is fuzzy thinking?
87(2)
4.2 Fuzzy sets
89(5)
4.3 Linguistic variables and hedges
94(3)
4.4 Operations of fuzzy sets
97(6)
4.5 Fuzzy rules
103(3)
4.6 Fuzzy inference
106(7)
4.7 Building a fuzzy expert system
113(12)
4.8 Summary
125(6)
Questions for review
126(1)
References
127(1)
Bibliography
127(4)
5 Frame-based expert systems
131(34)
5.1 Introduction, or what is a frame?
131(2)
5.2 Frames as a knowledge representation technique
133(5)
5.3 Inheritance in frame-based systems
138(4)
5.4 Methods and demons
142(4)
5.5 Interaction of frames and rules
146(3)
5.6 Buy Smart: a frame-based expert system
149(12)
5.7 Summary
161(4)
Questions for review
163(1)
References
163(1)
Bibliography
164(1)
6 Artificial neural networks
165(54)
6.1 Introduction, or how the brain works
165(3)
6.2 The neuron as a simple computing element
168(2)
6.3 The perceptron
170(5)
6.4 Multilayer neural networks
175(10)
6.5 Accelerated learning in multilayer neural networks
185(3)
6.6 The Hopfield network
188(8)
6.7 Bidirectional associative memory
196(4)
6.8 Self-organising neural networks
200(12)
6.9 Summary
212(7)
Questions for review
215(1)
References
216(3)
7 Evolutionary computation
219(40)
7.1 Introduction, or can evolution be intelligent?
219(1)
7.2 Simulation of natural evolution
219(3)
7.3 Genetic algorithms
222(10)
7.4 Why genetic algorithms work
232(3)
7.5 Case study: maintenance scheduling with genetic algorithms
235(7)
7.6 Evolution strategies
242(3)
7.7 Genetic programming
245(9)
7.8 Summary
254(5)
Questions for review
255(1)
References
256(1)
Bibliography
257(2)
8 Hybrid intelligent systems
259(42)
8.1 Introduction, or how to combine German mechanics with Italian love
259(2)
8.2 Neural expert systems
261(7)
8.3 Neuro-fuzzy systems
268(9)
8.4 ANFIS: Adaptive Neuro-Fuzzy Inference System
277(8)
8.5 Evolutionary neural networks
285(5)
8.6 Fuzzy evolutionary systems
290(6)
8.7 Summary
296(5)
Questions for review
297(1)
References
298(3)
9 Knowledge engineering
301(64)
9.1 Introduction, or what is knowledge engineering?
301(7)
9.2 Will an expert system work for my problem?
308(9)
9.3 Will a fuzzy expert system work for my problem?
317(6)
9.4 Will a neural network work for my problem?
323(20)
9.5 Will genetic algorithms work for my problem?
343(5)
9.6 Will a hybrid intelligent system work for my problem?
348(9)
9.7 Summary
357(8)
Questions for review
359(3)
References
362(3)
10 Data mining and knowledge discovery
365(60)
10.1 Introduction, or what is data mining?
365(4)
10.2 Statistical methods and data visualisation
369(5)
10.3 Principal component analysis
374(12)
10.4 Relational databases and database queries
386(5)
10.5 The data warehouse and multidimensional data analysis
391(10)
10.6 Decision trees
401(9)
10.7 Association rules and market basket analysis
410(8)
10.8 Summary
418(7)
Questions for review
420(1)
References
421(4)
Glossary 425(26)
Appendix: Al tools and vendors 451(20)
Index 471
Dr Michael Negnevitsky is a Professor in Electrical Engineering and Computer Science at the University of Tasmania, Australia. The book has developed from his lectures to undergraduates. Educated as an electrical engineer, Dr Negnevitskys many interests include artificial intelligence and soft computing. His research involves the development and application of intelligent systems in electrical engineering, process control and environmental engineering. He has authored and co-authored over 300 research publications including numerous journal articles, four patents for inventions and two books.
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