Fundamentals of the New Artificial Intelligence

by Munakata, Toshinori

• ISBN13: 9781846288388
• ISBN10: 184628838X
• Edition: 2nd
• Format: Hardcover
• Copyright: 2008-02-28
• Publisher: Springer-Verlag New York Inc

Summary

Artificial intelligence-broadly defined as the study of making computers perform tasks that require human intelligence-has grown rapidly as a field of research and industrial application in recent years. Whereas traditionally, Al used techniques drawn from symbolic models such as knowledge-based and logic programming systems, interest has grown in newer paradigms, notably neural networks, genetic algorithms, and fuzzy logic.

Author Biography

Professor Munakata is a leading figure in this field and has given courses on this topic extensively

Table of Contents

Preface	p. v
Introduction	p. 1
An Overview of the Field of Artificial Intelligence	p. 1
An Overview of the Areas Covered in this Book	p. 3
Neural Networks: Fundamentals and the Backpropagation Model	p. 7
What is a Neural Network?	p. 7
A Neuron	p. 7
Basic Idea of the Backpropagation Model	p. 8
Details of the Backpropagation Mode	p. 15
A Cookbook Recipe to Implement the Backpropagation Model	p. 22
Additional Technical Remarks on the Backpropagation Model	p. 24
Simple Perceptrons	p. 28
Applications of the Backpropagation Model	p. 31
General Remarks on Neural Networks	p. 33
Neural Networks: Other Models	p. 37
Prelude	p. 37
Associative Memory	p. 40
Hopfield Networks	p. 41
The Hopfield-Tank Model for Optimization Problems: The Basics	p. 46
One-Dimensional Layout	p. 46
Two-Dimensional Layout	p. 48
The Hopfield-Tank Model for Optimization Problems: Applications	p. 49
The N-Queen Problem	p. 49
A General Guideline to Apply the Hopfield-Tank Model to Optimization Problems	p. 54
Traveling Salesman Problem (TSP)	p. 55
The Kohonen Model	p. 58
Simulated Annealing	p. 63
Boltzmann Machines	p. 69
An Overview	p. 69
Unsupervised Learning by the Boltzmann Machine: The Basics Architecture	p. 70
Unsupervised Learning by the Boltzmann Machine: Algorithms	p. 76
Appendix. Derivation of Delta-Weights	p. 81
Genetic Algorithms and Evolutionary Computing	p. 85
What are Genetic Algorithms and Evolutionary Computing?	p. 85
Fundamentals of Genetic Algorithms	p. 87
A Simple Illustration of Genetic Algorithms	p. 90
A Machine Learning Example: Input-to-Output Mapping	p. 95
A Hard Optimization Example: the Traveling Salesman Problem (TSP)	p. 102
Schemata	p. 108
Changes of Schemata Over Generations	p. 109
Example of Schema Processing	p. 113
Genetic Programming	p. 116
Additional Remarks	p. 118
Fuzzy Systems	p. 121
Introduction	p. 121
Fundamentals of Fuzzy Sets	p. 123
What is a Fuzzy Set?	p. 123
Basic Fuzzy Set Relations	p. 125
Basic Fuzzy Set Operations and Their Properties	p. 126
Operations Unique to Fuzzy Sets	p. 128
Fuzzy Relations	p. 130
Ordinary (Nonfuzzy) Relations	p. 130
Fuzzy Relations Defined on Ordinary Sets	p. 133
Fuzzy Relations Derived from Fuzzy Sets	p. 138
Fuzzy Logic	p. 138
Ordinary Set Theory and Ordinary Logic	p. 138
Fuzzy Logic Fundamentals	p. 139
Fuzzy Control	p. 143
Fuzzy Control Basics	p. 143
Case Study: Controlling Temperature with a Variable Heat Source	p. 150
Extended Fuzzy if-then Rules Tables	p. 152
A Note on Fuzzy Control Expert Systems	p. 155
Hybrid Systems	p. 156
Fundamental Issues	p. 157
Additional Remarks	p. 158
Rough Sets	p. 162
Introduction	p. 162
Review of Ordinary Sets and Relations	p. 165
Information Tables and Attributes	p. 167
Approximation Spaces	p. 170
Knowledge Representation Systems	p. 176
More on the Basics of Rough Sets	p. 180
Additional Remarks	p. 188
Case Study and Comparisons with Other Techniques	p. 191
Rough Sets Applied to the Case Study	p. 192
ID3 Approach and the Case Study	p. 195
Comparisons with Other Techniques	p. 202
Chaos	p. 206
What is Chaos?	p. 206
Representing Dynamical Systems	p. 210
Discrete dynamical systems	p. 210
Continuous dynamical systems	p. 212
State and Phase Spaces	p. 218
Trajectory, Orbit and Flow	p. 218
Cobwebs	p. 221
Equilibrium Solutions and Stability	p. 222
Attractors	p. 227
Fixed-point attractors	p. 228
Periodic attractors	p. 228
Quasi-periodic attractors	p. 230
Chaotic attractors	p. 233
Bifurcations	p. 234
Fractals	p. 238
Applications of Chaos	p. 242
Index	p. 247
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