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
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
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
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
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
S.？ 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
Evolutionary computation
7.1 Introduction, or can evolution be intelligent？
7.2 Simulation of natural evolution
7.3 Genetic algorithms
……
Hybrid intelligent systems
Knowledge engineering
Data mining and knowledge discovery
Glossary
Appendix: AI tools and vendors
Index2100433B
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