Learning Structurally Analogous Tasks

  • Paul W. Munro
Part of the Lecture Notes in Computer Science book series (LNCS, volume 5164)


A method for training overlapping feedforward networks on analogous tasks is extended and analyzed. The learning dynamics of simultaneous (interlaced) training of similar tasks interact at the shared connections of the networks. The influence of one task on the other can be studied by examining the output of one network in response to a stimulus to the other network. Using backpropagation to train networks with shared hidden layers, a “crosstraining” mechanism for specifying corresponding components between structurally similar environments is introduced. Analysis of the resulting mappings reveals the potential for analogical inference.


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Copyright information

© Springer-Verlag Berlin Heidelberg 2008

Authors and Affiliations

  • Paul W. Munro
    • 1
  1. 1.School of Information SciencesUniversity of PittsburghPittsburghUSA

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