Artifical intelligence : a guide to intelligent systems / Michael Negnevitsky.

Table of contents

1.Introduction to knowledge based intelligent systems
1.1 Intelligent machines or what machines can do
1.2 The history of artificial intelligence3, or from the dark ages to knowledge based systems
1.3 Summary

2. Rule-based expert
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
etc.

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
etc.

4. Fuzzy expert systems
4.1 Introduction, or what is fuzzy thinking?
4.2 Fuzzy sets
4.3 Linguistic variable and hedges
4.4 Operations of fuzzy sets
etc.

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
etc.

6. Artificial 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
etc.

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?
etc.

8. Hybrid intelligent systems
8.1 Introduction, or how to combine German mechanics with Italian love
8.2 Neural expert systems
8.3 Neural-fuzzy systems
8.4 ANFIS
etc.

9. Knowledge engineering and data mining
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?
etc.

References : p. 363 - 364 . _ Glossary : p. 365 - 389 . _ Index : p. 407 - 415