Artificial intelligence : a guide to intelligent systems = 人工智能 : 智能系统指南 / 3rd ed.

《人工智能智能系统指南(英文版.第3版)》

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

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.s 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: al tools and vendors

index