TR announcement

[Frank Smieja]

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             The Pandemonium System of Reflective Agents (17 pages)

			by Frank Smieja
			GMD, Bonn, Germany

ABSTRACT: The Pandemonium system of reflective MINOS agents solves
problems by automatic dynamic modularization of the input space. The
agents contain feed-forward neural networks which adapt using the
back-propagation algorithm. We demonstrate the performance of
Pandemonium on various categories of problems.  These include learning
continuous functions with discontinuities, separating two spirals,
learning the parity function, and optical character recognition.  It
is shown how strongly the advantages gained from using a
modularization technique depend on the nature of the problem.  The
superiority of the Pandemonium method over a single net on the first
two test categories is contrasted with its limited advantages for the
second two categories.  In the first case the system converges quicker
with modularization and is seen to lead to simpler solutions. For the
second case the problem is not significantly simplified through flat
decomposition of the input space, although convergence is still
quicker.


	-Frank Smieja

Gesellschaft fuer Mathematik und Datenverarbeitung (GMD)
GMD-FIT.KI.AS, Schloss Birlinghoven, 53757 St Augustin, Germany.
Tel: +49 2241-142214		email: smieja at gmd.de