Neural-Symbolic Learning Systems
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This book provides a comprehensive introduction to the field of neural-symbolic learning systems, and an invaluable overview of the latest research issues in this area. It is divided into three sections, covering the main topics of neural-symbolic integration - theoretical advances in knowledge representation and learning, knowledge extraction from trained neural networks, and inconsistency handling in neural-symbolic systems. Each section provides a balance of theory and practice, giving the results of applications using real-world problems in areas such as DNA sequence analysis, power systems fault diagnosis, and software requirements specifications.
Neural-Symbolic Learning Systems will be invaluable reading for researchers and graduate students in Engineering, Computing Science, Artificial Intelligence, Machine Learning and Neurocomputing. It will also be of interest to Intelligent Systems practitioners and anyone interested in applications of hybrid artificial intelligence systems.
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
2.3.1 Architectures
2.3.2 Learning Strategy
2.3.3 Recurrent Networks
2.4 Logic Programming
2.4.1 What is Logic Programming?
2.4.2 Fixpoints and Definite Programs
2.5 Nonmonotonic Reasoning
2.5.1 Stable Models and Acceptable Programs
2.6 Belief Revision
2.6.1 Truth Maintenance Systems
2.6.2 Compromise Revision
I. Knowledge Refinement in Neural Networks
3. Theory Refinement in Neural Networks
3.1 Inserting Background Knowledge
3.2 Massively Parallel Deduction
3.3 Performing Inductive Learning
3.4 Adding Classical Negation
3.5 Adding Metalevel Priorities
3.6 Summary and Further Reading
4. Experiments on Theory Refinement
4.1 DNA Sequence Analysis
4.2 Power Systems Fault Diagnosis
4.3.Discussion
4.4.Appendix
II. Knowledge Extraction from Neural Networks
5. Knowledge Extraction from Trained Networks
5.1 The Extraction Problem
5.2 The Case of Regular Networks
5.2.1 Positive Networks
5.2.2 Regular Networks
5.3 The General Case Extraction
5.3.1 Regular Subnetworks
5.3.2 Knowledge Extraction from Subnetworks
5.3.3 Assembling the Final Rule Set
5.4 Knowledge Representation Issues
5.5 Summary and Further Reading
6. Experiments on Knowledge Extraction
6.1 Implementation
6.2 The Monk's Problems
6.3 DNA Sequence Analysis
6.4 Power Systems Fault Diagnosis
6.5 Discussion
III. Knowledge Revision in Neural Networks
7. Handling Inconsistencies in Neural Networks
7.1 Theory Revision in Neural Networks
7.1.1The Equivalence with Truth Maintenance Systems
7.1.2Minimal Learning
7.2 Solving Inconsistencies in Neural Networks
7.2.1 Compromise Revision
7.2.2 Foundational Revision
7.2.3 Nonmonotonic Theory Revision
7.3 Summary of the
8. Experiments on Handling Inconsistencies
8.1 Requirements Specifications Evolution as Theory Refinement
8.1.1Analysing Specifications
8.1.2Revising Specifications
8.2 The Automobile Cruise Control System
8.2.1Knowledge Insertion
8.2.2Knowledge Revision: Handling Inconsistencies
8.2.3Knowledge Extraction
8.3 Discussion
8.4 Appendix
9. Neural-Symbolic Integration: The Road Ahead
9.1 Knowledge Extraction
9.2 Adding Disjunctive Information
9.3 Extension to the First-Order Case
9.4 Adding Modalities
9.5 New Preference Relations
9.6 A Proof Theoretical Approach
9.7 The "Forbidden Zone" [Amax, Amin]
9.8 Acceptable Programs and Neural Networks
9.9 Epilogue
- Autoren: Artur S. d'Avila Garcez , Krysia B. Broda , Dov M. Gabbay
- 2002, 2002, 271 Seiten, 30 Schwarz-Weiß-Abbildungen, Maße: 15,5 x 23,5 cm, Kartoniert (TB), Englisch
- Verlag: Springer
- ISBN-10: 1852335122
- ISBN-13: 9781852335120
- Erscheinungsdatum: 06.08.2002
Machine Learning für Softwareentwickler
Paolo Perrotta
34.90 €
Soft Computing and Fractal Theory for Intelligent Manufacturing
Oscar Castillo, Patricia Melin
176.54 €
176.54 €
Neural Networks and the Financial Markets
Jimmy Shadbolt
160.45 €
107.50 €
139.09 €
149.79 €
Neuro-Fuzzy-Systeme
Christian Borgelt, Rudolf Kruse, Frank Klawonn, Detlef Nauck
39.99 €
Turing's Connectionism
Christof Teuscher
106.99 €
Fahrzeugsicherheit und automatisiertes Fahren
Michael Botsch, Wolfgang Utschick
39.99 €
Pattern Classification
Shigeo Abe
149.79 €
Introduction to Neuro-Fuzzy Systems
Robert Fuller
106.95 €
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