Artificial Intelligence A Guide to Intelligent Systems

ISBN-10: 0321204662
ISBN-13: 9780321204660
Edition: 2nd 2005 (Revised)
List price: $180.40
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Description: Keeping the maths to a minimum, Negnevitsky explains the principles of AI, demonstrates how systems are built, what they are useful for and how to choose the right tool for the job.

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Book details

List price: $180.40
Edition: 2nd
Copyright year: 2005
Publisher: Pearson Education
Publication date: 11/2/2004
Binding: Hardcover
Pages: 440
Size: 6.50" wide x 9.50" long x 1.25" tall
Weight: 1.716
Language: English

Keeping the maths to a minimum, Negnevitsky explains the principles of AI, demonstrates how systems are built, what they are useful for and how to choose the right tool for the job.

Preface
Preface to the second edition
Acknowledgements
Introduction to knowledge-based intelligent systems
Intelligent machines, or what machines can do
The history of artificial intelligence, or from the 'Dark Ages' to knowledge-based systems
Summary
Questions for review
References
Rule-based expert systems
Introduction, or what is knowledge?
Rules as a knowledge representation technique
The main players in the expert system development team
Structure of a rule-based expert system
Fundamental characteristics of an expert system
Forward chaining and backward chaining inference techniques
MEDIA ADVISOR: a demonstration rule-based expert system
Conflict resolution
Advantages and disadvantages of rule-based expert systems
Summary
Questions for review
References
Uncertainty management in rule-based expert systems
Introduction, or what is uncertainty?
Basic probability theory
Bayesian reasoning
FORECAST: Bayesian accumulation of evidence
Bias of the Bayesian method
Certainty factors theory and evidential reasoning
FORECAST: an application of certainty factors
Comparison of Bayesian reasoning and certainty factors
Summary
Questions for review
References
Fuzzy expert systems
Introduction, or what is fuzzy thinking?
Fuzzy sets
Linguistic variables and hedges
Operations of fuzzy sets
Fuzzy rules
Fuzzy inference
Building a fuzzy expert system
Summary
Questions for review
References
Bibliography
Frame-based expert systems
Introduction, or what is a frame?
Frames as a knowledge representation technique
Inheritance in frame-based systems
Methods and demons
Interaction of frames and rules
Buy Smart: a frame-based expert system
Summary
Questions for review
References
Bibliography
Artificial neural networks
Introduction, or how the brain works
The neuron as a simple computing element
The perceptron
Multilayer neural networks
Accelerated learning in multilayer neural networks
The Hopfield network
Bidirectional associative memory
Self-organising neural networks
Summary
Questions for review
References
Evolutionary computation
Introduction, or can evolution be intelligent?
Simulation of natural evolution
Genetic algorithms
Why genetic algorithms work
Case study: maintenance scheduling with genetic algorithms
Evolution strategies
Genetic programming
Summary
Questions for review
References
Bibliography
Hybrid intelligent systems
Introduction, or how to combine German mechanics with Italian love
Neural expert systems
Neuro-fuzzy systems
ANFIS: Adaptive Neuro-Fuzzy Inference System
Evolutionary neural networks
Fuzzy evolutionary systems
Summary
Questions for review
References
Knowledge engineering and data mining
Introduction, or what is knowledge engineering?
Will an expert system work for my problem?
Will a fuzzy expert system work for my problem?
Will a neural network work for my problem?
Will genetic algorithms work for my problem?
Will a hybrid intelligent system work for my problem?
Data mining and knowledge discovery
Summary
Questions for review
References
Glossary
Appendix
Index

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