Artificial Intelligence A Guide to Intelligent Systems 2nd Edition by Stuart Jonathan Russell, Peter Norvig ISBN 0321204662 9780321204660

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ISBN 10: 0321204662 
ISBN 13: 9780321204660
Author: Stuart Jonathan Russell, Peter Norvig

Artificial Intelligence is one of the most rapidly evolving subjects within the computing/engineering curriculum, with an emphasis on creating practical applications from hybrid techniques. Despite this, the traditional textbooks continue to expect mathematical and programming expertise beyond the scope of current undergraduates and focus on areas not relevant to many of today’s courses. 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 will be described and program examples will be 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 contempory coverage means more advanced students will benefit by discovering the latest state-of-the-art techniques.

Artificial Intelligence A Guide to Intelligent Systems 2nd Table of contents:

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
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?
Summary
Questions for review
References
Data mining and knowledge discovery
Introduction, or what is data mining?
Statistical methods and data visualisation
Principal component analysis
Relational databases and database queries
The data warehouse and multidimensional data analysis
Decision trees
Association rules and market basket analysis
Summary
Questions for review

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