Neural-Symbolic Learning Systems
Foundations and Applications
Published on 6. August 2002
Book
Paperback/Softback
XIV, 271 pages
978-1-85233-512-0 (ISBN)
Description
Computing Science and Artificial Intelligence are concerned with producing devices that help and/or replace human beings in their daily activities. To be successful, adequate modelling of these activities needs to be carried out and this has accelerated the development of both old and new disciplines, including Logic and Computation, Neural Networks, Genetic Algorithms and Probabilistic/Casual Networks. This book looks at how these techniques could complement each other and how, by understanding the role of each in a particular application, we can pave the way towards the development of more effective intelligent systems.
More details
Series
Edition
2002 ed.
Language
English
Place of publication
London
United Kingdom
Target group
Professional and scholarly
Research
Illustrations
30 s/w Abbildungen
XIV, 271 p. 30 illus.
Dimensions
Height: 235 mm
Width: 155 mm
Thickness: 16 mm
Weight
441 gr
ISBN-13
978-1-85233-512-0 (9781852335120)
DOI
10.1007/978-1-4471-0211-3
Schweitzer Classification
Persons
Content
1. Introduction and Overview.- 1.1 Why Integrate Neurons and Symbols?.- 1.2 Strategies of Neural-Symbolic Integration.- 1.3 Neural-Symbolic Learning Systems.- 1.4 A Simple Example.- 1.5 How to Read this Book.- 1.6 Summary.- 2. Background.- 2.1 General Preliminaries.- 2.2 Inductive Learning.- 2.3 Neural Networks.- 2.4 Logic Programming.- 2.5 Nonmonotonic Reasoning.- 2.6 Belief Revision.- I. Knowledge Refinement in Neural Networks.- 3. Theory Refinement in Neural Networks.- 4. Experiments on Theory Refinement.- II. Knowledge Extraction from Neural Networks.- 5. Knowledge Extraction from Trained Networks.- 6. Experiments on Knowledge Extraction.- III. Knowledge Revision in Neural Networks.- 7. Handling Inconsistencies in Neural Networks.- 8. Experiments on Handling Inconsistencies.- 9. Neural-Symbolic Integration: The Road Ahead.