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Perspectives of Neural-Symbolic Integration

  • Barbara Hammer
  • Pascal Hitzler

Part of the Studies in Computational Intelligence book series (SCI, volume 77)

Table of contents

  1. Front Matter
    Pages I-XIII
  2. Structured Data and Neural Networks

    1. Front Matter
      Pages 1-5
    2. Craig Saunders, Anthony Demco
      Pages 7-22
    3. Fabrizio Costa, Sauro Menchetti, Paolo Frasconi
      Pages 23-48
    4. Tayfun Gürel, Luc De Raedt, Stefan Rotter
      Pages 49-65
    5. Peter Tiňo, Barbara Hammer, Mikael Bodén
      Pages 95-133
    6. Helge Ritter, Robert Haschke, Jochen J. Steil
      Pages 159-178
  3. Logic and Neural Networks

    1. Front Matter
      Pages 179-182
    2. Sebastian Bader, Pascal Hitzler, Steffen Hölldobler, Andreas Witzel
      Pages 205-232
    3. Helmar Gust, Kai-Uwe Kühnberger, Peter Geibel
      Pages 233-264
    4. Ekaterina Komendantskaya, Máire Lane, Anthony Karel Seda
      Pages 283-313
  4. Back Matter
    Pages 315-319

About this book

Introduction

The human brain possesses the remarkable capability of understanding, interpreting, and producing language, structures, and logic. Unlike their biological counterparts, artificial neural networks do not form such a close liason with symbolic reasoning: logic-based inference mechanisms and statistical machine learning constitute two major and very different paradigms in artificial intelligence with complementary strengths and weaknesses. Modern application scenarios in robotics, bioinformatics, language processing, etc., however require both the efficiency and noise-tolerance of statistical models and the generalization ability and high-level modelling of structural inference meachanisms. A variety of approaches has therefore been proposed for combining the two paradigms.

This carefully edited volume contains state-of-the-art contributions in neural-symbolic integration, covering `loose' coupling by means of structure kernels or recursive models as well as `strong' coupling of logic and neural networks. It brings together a representative selection of results presented by some of the top researchers in the field, covering theoretical foundations, algorithmic design, and state-of-the-art applications in robotics and bioinformatics.

Keywords

Computational Intelligence Markov Neural-symbolic integration algorithms architecture artificial neural network bioinformatics classification cognition learning logic model modeling robot robotics

Editors and affiliations

  • Barbara Hammer
    • 1
  • Pascal Hitzler
    • 2
  1. 1.University ClausthalGermany
  2. 2.University of KarlsruheGermany

Bibliographic information

  • DOI https://doi.org/10.1007/978-3-540-73954-8
  • Copyright Information Springer-Verlag Berlin Heidelberg 2007
  • Publisher Name Springer, Berlin, Heidelberg
  • eBook Packages Engineering
  • Print ISBN 978-3-540-73953-1
  • Online ISBN 978-3-540-73954-8
  • Series Print ISSN 1860-949X
  • Buy this book on publisher's site
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