Technical report available

[stefano nolfi]

The following technical report is now available. The paper
has been submitted to ICGA-91.

Send request to stiva at irmkant.Bitnet
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                        AUTO-TEACHING:
        NETWORKS THAT DEVELOP THEIR OWN TEACHING INPUT

              Stefano Nolfi       Domenico Parisi
                    Institute of Psychology
                          CNR - Rome
                  E-mail: stiva at irmkant.Bitnet


                         ABSTRACT

   Back-propagation  learning   (Rumelhart,  Hinton   and
   Williams, 1986) is a useful research tool but it has a
   number of  undesiderable features such  as  having the
   experimenter  decide  from   outside  what  should  be
   learned.  We  describe  a  number  of  simulations  of
   neural  networks  that  internally  generate their own
   teaching  input. The  networks  generate the  teaching
   input  by   trasforming   the  network  input  through
   connection weights that  are  evolved using a  form of
   genetic algorithm. What results is an innate (evolved)
   capacity not to behave efficiently  in  an environment
   but  to  learn  to behave efficiently. The analysis of
   what  these  networks  evolve  to   learn  shows  some
   interesting results.


references

Rumelhart, D.E., Hinton G.E., and   Williams, R.J. (1986).
Learning internal representations by error propagation. In
D.E.  Rumelhart,  and J.L.  McClelland,  (eds.),  Parallel
Distributed Processing.  Vol.1:  Foundations.   Cambridge,
Mass.: MIT Press.