EXTRACTING REGULARITIES IN SPACE AND TIME THROUGH A CASCADE OF PREDICTION 
 NETWORKS: THE CASE OF A MOBILE ROBOT NAVIGATING IN A STRUCTURED ENVIRONMENT 


 			     Stefano Nolfi
           Institute of Psychology, National Research Council
                    Viale Marx 15, 00137 Rome, Italy
                  Email:stefano@kant.irmkant.rm.cnr.it
                http://kant.irmkant.rm.cnr.it/nolfi.html

                               Jun Tani
                  Sony Computer Science Laboratory Inc.
            Takanawa Muse Building, 3-14-13 Higashi-gotanda, 
                    Shinagawa-ku, Tokyo, 141 Japan
                      Email: tani@csl.sony.co.jp
             http://www.csl.sony.co.jp/person/tani.html


We propose that  the ability to extract regularities from time series through 
prediction learning can  be enhanced if we use a hierarchical architecture in 
which higher layers are trained to predict the internal state of lower layers 
when such states change significantly. This hierarchical organization has two 
functions:  (a)  it  forces  the  system  to  progressively  re-code  sensory 
information  so  as  to  enhance  useful  regularities and filter out useless 
information; (b) it progressively reduces  the length  of the sequences which 
should be predicted going from lower to  higher layers. This, in turn, allows 
higher levels  to  extract higher  level regularities which are hidden at the 
sensory level. By  training  an architecture of this type to predict the next 
sensory state  of  a robot navigating in a environment divided into two rooms 
we show how the  first level prediction layer extracts low level regularities 
such as 'walls', 'corners', and 'corridors' while the second level prediction 
layer extracts  higher  level regularities such as 'the left side wall of the 
large room'. The  extraction  of  these  regularities allows the robot and to 
localize  its  position  in  the  environment  and  to  detect changes in the 
environment (e.g. the presence of  a  new  object or the fact that a door has 
been closed).