TR available

[Noel Sharkey]

TECH REPORT AVAILABLE: Computer Science TR R257

ADAPTIVE GENERALISATION AND THE TRANSFER OF KNOWLEDGE

Noel E. Sharkey and Amanda J.C. Sharkey

Center for Connection Science, University of Exeter


Adaptive generalisation is the ability to use prior knowledge in the
performance of novel tasks.  Thus, if we are to model intelligent behaviour
with neural nets, they must be able to generalise across task domains.
Our objective is to elucidate the aetiology of transfer of
information between connectionist nets. First, a method is described
that provides a standardised score for the quantification of how much
task structure a net has extracted, and to what degree knowledge has
been transferred between tasks.  This method is then applied in three
simulation studies to examine Input-to-Hidden (IH) and
Hidden-to-Output (HO) decision hyperplanes as determinants of transfer
effects.  In the first study, positive transfer is demonstrated
between functions that require the vertices of their input spaces to
be divided similarly, and negative transfer between functions that
require decision regions of different shapes.  In the other two
studies, input and output similarity are varied independently in a
series of paired associate learning tasks.  Further explanation of
transfer effects is provided through the use of a new technique that
permits better visualisation of the entire computational space by
showing both the relative position of inputs in Hidden Unit space, and
the HO decision regions implemented by a set of weights.

*
This research was supported by an award from the Economic and Social Research
Council, Grant No R000233441.  An earlier version of this paper appears in
the Proceedings of the Second Irish Neural Networks Conference, Belfast 1992.
The current version will appear in an AI review special issue on Connectionism.


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