The Core Method: Connectionist Model Generation

  • Sebastian Bader
  • Steffen Hölldobler
Part of the Lecture Notes in Computer Science book series (LNCS, volume 4132)


Knowledge based artificial networks networks have been applied quite successfully to propositional knowledge representation and reasoning tasks. However, as soon as these tasks are extended to structured objects and structure-sensitive processes it is not obvious at all how neural symbolic systems should look like such that they are truly connectionist and allow for a declarative reading at the same time. The core method aims at such an integration. It is a method for connectionist model generation using recurrent networks with feed-forward core. After an introduction to the core method, this paper will focus on possible connectionist representations of structured objects and their use in structure-sensitive reasoning tasks.


Logic Program Structure Object Piecewise Constant Function Recurrent Network Ground Atom 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.


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Copyright information

© Springer-Verlag Berlin Heidelberg 2006

Authors and Affiliations

  • Sebastian Bader
    • 1
  • Steffen Hölldobler
    • 1
  1. 1.International Center for Computational LogicTechnische Universität DresdenDresdenGermany

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