Résumé
Artificial Intelligence: A Guide to Intelligent Systems
provides a practical introduction to artificial
intelligence that is less mathematically rigorous than
other books on the market. Micahel Negnevitsky neatly
explains the principles, demonstrates how systems are
built, what they are useful for and how to choose the right
tool for the job. No particular programming language
knowledge is assumed and mathematical prerequisites are
kept to a minimum. The book's broad coverage of expert
systems, artificial neural networks, fuzzy systems and
evolutionary computation shows how intelligent systems work
together. Other areas of discussion include rule-based
expert systems, frame-based expert systems, hybrid
intelligent systems, knowledge engineering, and data
mining. This is appropriate for programmers looking for an
overview of all facets of artificial intelligence. It does
not require knowledge of a particular language.
2. Rule-Based Expert Systems.
3. Uncertainty Management in Rule-Based Expert Systems.
4. Fuzzy Expert Systems.
5. Frame-Based ExpertSystems.
6. Artificial Neural Networks.
7. Evolutionary Computation.
8. Hybrid Intelligent Systems.
9. Knowledge Engineering and Data Mining.
Glossary.
Appendix.
Index.
Contents
1. Introduction to Knowledge-base Intelligent
Systems.
Intelligent machines, or
what machines can do.
The history of artificial intelligence, or from the "Dark Ages" to knowledge-based systems.
Summary.
The history of artificial intelligence, or from the "Dark Ages" to knowledge-based systems.
Summary.
2. 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.
THERMOSTAT: a demonstration of a rule-based expert system.
Conflict resolution.
Advantages and disadvantages of rule-based expert systems.
Summary.
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.
THERMOSTAT: a demonstration of a rule-based expert system.
Conflict resolution.
Advantages and disadvantages of rule-based expert systems.
Summary.
3. 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.
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.
4. 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.
Fuzzy sets.
Linguistic variables and hedges.
Operations of fuzzy sets.
Fuzzy rules.
Fuzzy inference.
Building a fuzzy expert system.
Summary.
5. Frame-Based ExpertSystems.
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.
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.
6. 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.
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.
7. Evolutionary Computation.
Introduction, or can
evolution be intelligent?
Simulation of natural evolution.
Genetic algorithms.
Why genetic algorithms work.
Case study: maintenance and scheduling with genetic algorithms.
Evolution strategies.
Genetic programming.
Summary.
Simulation of natural evolution.
Genetic algorithms.
Why genetic algorithms work.
Case study: maintenance and scheduling with genetic algorithms.
Evolution strategies.
Genetic programming.
Summary.
8. 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.
Neural expert systems.
Neuro-fuzzy systems.
ANFIS: Adaptive Neuro-Fuzzy Inference System.
Evolutionary neural networks.
Fuzzy evolutionary systems.
Summary.
9. 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?
Data mining and knowledge discovery.
Summary.
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?
Data mining and knowledge discovery.
Summary.
Glossary.
Appendix.
Index.
Caractéristiques techniques
| PAPIER | |
| Éditeur(s) | Addison Wesley |
| Auteur(s) | Michael Negnevitsky |
| Parution | 01/09/2001 |
| Nb. de pages | 394 |
| Format | 16 x 24 |
| Couverture | Relié |
| Poids | 746g |
| Intérieur | Noir et Blanc |
| EAN13 | 9780201711592 |
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