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Neural-Symbolic Learning Systems : Foundations and Applications /

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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 Author: D'Avila Garcez, Artur S.
Corporate Author: SpringerLink (Online service)
Other Authors: Broda, Krysia B., Gabbay, Dov M.
Format: eBook
Language:English
Published: London : Springer London : Imprint : Springer, 2002.
Series:Perspectives in neural computing.
Subjects:
Computer science.
Information systems.
Artificial intelligence.
Telecommunication.
Electronic books.
Online Access:Connect to the full text of this electronic book
Table of Contents:
  • Introduction and Overview
  • Background
  • Part I. Knowledge Refinement in Neural Networks: Theory Refinement in Neural Networks. Experiments on Theory Refinement
  • Part II. Knowledge Extraction from Neural Networks: Knowledge Extraction from Trained Networks. Experiments on Knowledge Extraction
  • Part III. Knowledge Revision in Neural Networks: Handling Inconsistencies in Neural Networks. Experiments on Handling Inconsistencies
  • Neural-Symbolic Integration: The Road Ahead
  • References
  • Index.