Neural-Symbolic Learning Systems Foundations and Applications

Neural-Symbolic Learning Systems Foundations and Applications / by Artur S. d'Avila Garcez, Krysia B. Broda, Dov M. Gabbay.

Artificial Intelligence is concerned with producing devices that help or replace human beings in their daily activities. Neural-symbolic learning systems play a central role in this task by combining, and trying to benefit from, the advantages of both the neural and symbolic paradigms of artificial...

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Bibliographic Details
Main Authors: d'Avila Garcez, Artur S. (Author), Broda, Krysia B. (Author), Gabbay, Dov M. (Author)
Corporate Author: SpringerLink (Online service)
Format: eBook
Language:English
Published: London : Springer London : Imprint: Springer, 2002.
Edition:1st ed. 2002.
Series:Perspectives in Neural Computing,
Springer eBook Collection.
Subjects:
Artificial intelligence.
Computers.
Electrical engineering.
Electronic resources (E-books)
Online Access:Click to view e-book
Holy Cross Note:Loaded electronically.
Electronic access restricted to members of the Holy Cross Community.
Table of Contents:
  • 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.

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