AUTO-TEACHING: NETWORKS THAT DEVELOP THEIR OWN TEACHING INPUT

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.