Connectionist symbol processing: any progress?

[Paolo Frasconi]

Adaptive techniques for dealing with structured information have
recently emerged. In particular, algorithms and architectures for
learning directed ordered acyclic graphs (DOAGs) are available and
they have been shown to be effective in some application domains such
as automated reasoning and pattern recognition. The basic idea is an
extension of recurrent neural networks from sequences (which can be
seen as a very special case of graphs having a linear-chain shape) to
graphs. A generalization of backpropagation through time is available
for acyclic graphs.  Models for data structures can be conveniently
represented in a graphical formalism which makes it simple to
understand them as special cases of belief networks.  There is still
quite a lot of work to be done in this area and the interest is
expanding. Last year we had a NIPS workshop on adaptive processing of
data structures.  Further details and links to papers can be found in
the web page http://www.dsi.unifi.it/~paolo/datas (also mirrowed at
http://www.uow.edu.au/~paolo/datas).


Paolo Frasconi       paolo at uow.edu.au
Marco Gori           marco at ing.unisi.it
Alessandro Sperduti  perso at di.unipi.it


     Paolo Frasconi                      <paolo at uow.edu.au>
   Visiting  Lecturer
 Faculty of Informatics 
University of Wollongong             Phone: +61 2 4221 3121 (office)
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