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Neural-Symbolic Learning Systems

Foundations and Applications

  • Artur S. d’Avila Garcez
  • Krysia B. Broda
  • Dov M. Gabbay

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

Table of contents

  1. Front Matter
    Pages i-xiii
  2. Introduction and Overview

    1. Artur S. d’Avila Garcez, Krysia B. Broda, Dov. M. Gabbay
      Pages 1-12
    2. 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
    2. Artur S. d’Avila Garcez, Krysia B. Broda, Dov. M. Gabbay
      Pages 43-85
    3. 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
    2. Artur S. d’Avila Garcez, Krysia B. Broda, Dov. M. Gabbay
      Pages 113-158
    3. 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
    2. Artur S. d’Avila Garcez, Krysia B. Broda, Dov. M. Gabbay
      Pages 183-208
    3. Artur S. d’Avila Garcez, Krysia B. Broda, Dov. M. Gabbay
      Pages 209-233
    4. Artur S. d’Avila Garcez, Krysia B. Broda, Dov. M. Gabbay
      Pages 235-252
  6. Back Matter
    Pages 253-271

About this book

Introduction

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.

Keywords

Artificial neural networks Hybrid systems Machine learning Neural-symbolic integration artificial intelligence intelligence intelligent systems knowledge representation learning logic logic programming nonmonotonic reasoning

Authors and affiliations

  • Artur S. d’Avila Garcez
    • 1
  • Krysia B. Broda
    • 2
  • Dov M. Gabbay
    • 3
  1. 1.Department of ComputingCity UniversityLondon
  2. 2.Department of ComputingImperial CollegeLondon
  3. 3.Department of Computer ScienceKing’s CollegeLondon

Bibliographic information

  • DOI https://doi.org/10.1007/978-1-4471-0211-3
  • Copyright Information Springer-Verlag London Limited 2002
  • Publisher Name Springer, London
  • eBook Packages Springer Book Archive
  • Print ISBN 978-1-85233-512-0
  • Online ISBN 978-1-4471-0211-3
  • Series Print ISSN 1431-6854
  • Buy this book on publisher's site
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