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Neural-Symbolic Learning Systems

Foundations and Applications

  • Book
  • © 2002

Overview

Authors:
  1. Artur S. d’Avila Garcez

    1. Department of Computing, City University, London

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    You can also search for this author in PubMed   Google Scholar

  2. Krysia B. Broda

    1. Department of Computing, Imperial College, London

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    You can also search for this author in PubMed   Google Scholar

  3. Dov M. Gabbay

    1. Department of Computer Science, King’s College, London

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  • Provides the first single-source introduction to the field of knowledge-based neuro-computing
  • Includes real-world applications of neural-symbolic integration systems
  • Includes supplementary material: sn.pub/extras

Part of the book series: Perspectives in Neural Computing (PERSPECT.NEURAL)

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Table of contents (9 chapters)

  1. Front Matter

    Pages i-xiii
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  2. Introduction and Overview

    1. Introduction and Overview

      • Artur S. d’Avila Garcez, Krysia B. Broda, Dov. M. Gabbay
      Pages 1-12

    2. Background

      • Artur S. d’Avila Garcez, Krysia B. Broda, Dov. M. Gabbay
      Pages 13-40

  3. Knowledge Refinement in Neural Networks

    1. Front Matter

      Pages 41-41
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    2. Theory Refinement in Neural Networks

      • Artur S. d’Avila Garcez, Krysia B. Broda, Dov. M. Gabbay
      Pages 43-85

    3. Experiments on Theory Refinement

      • Artur S. d’Avila Garcez, Krysia B. Broda, Dov. M. Gabbay
      Pages 87-110

  4. Knowledge Extraction from Neural Networks

    1. Front Matter

      Pages 111-111
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    2. Knowledge Extraction from Trained Networks

      • Artur S. d’Avila Garcez, Krysia B. Broda, Dov. M. Gabbay
      Pages 113-158

    3. Experiments on Knowledge Extraction

      • Artur S. d’Avila Garcez, Krysia B. Broda, Dov. M. Gabbay
      Pages 159-179

  5. Knowledge Revision in Neural Networks

    1. Front Matter

      Pages 181-181
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    2. Handling Inconsistencies in Neural Networks

      • Artur S. d’Avila Garcez, Krysia B. Broda, Dov. M. Gabbay
      Pages 183-208

    3. Experiments on Handling Inconsistencies

      • Artur S. d’Avila Garcez, Krysia B. Broda, Dov. M. Gabbay
      Pages 209-233

    4. Neural-Symbolic Integration: The Road Ahead

      • Artur S. d’Avila Garcez, Krysia B. Broda, Dov. M. Gabbay
      Pages 235-252

  6. Back Matter

    Pages 253-271
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Keywords

About this book

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 intelligence.
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.

Authors and Affiliations

  • Department of Computing, City University, London

    Artur S. d’Avila Garcez

  • Department of Computing, Imperial College, London

    Krysia B. Broda

  • Department of Computer Science, King’s College, London

    Dov M. Gabbay

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