TR (reflective)

[Frank Smieja]

-) *******************************************************************
-) 	REFLECTIVE MODULAR NEURAL NETWORK SYSTEMS
-) 	
-) 		F. J. Smieja and H. Muehlenbein 
-) 
-) German National Research Centre for Computer Science (GMD)
-) 		Schlo{\ss} Birlinghoven, 
-) 		  5205 St. Augustin 1, 
-) 			Germany.
-) 
-) 			ABSTRACT
-) 
-) Many of the current artificial neural network systems have serious
-) limitations, concerning accessibility, flexibility, scaling and
-) reliability. In order to go some way to removing these we suggest a
-) {\it reflective neural network architecture}. In such an architecture,
-) the modular structure is the most important element.  The
-) building-block elements are called ``\MINOS'' modules.  They perform
-) {\it self-observation\/} and inform on the current level of
-) development, or scope of expertise, within the module.  A {\it
-) Pandemonium\/} system integrates such submodules so that they work
-) together to handle mapping tasks.  Network complexity limitations are
-) attacked in this way with the Pandemonium problem decomposition
-) paradigm, and both static and dynamic unreliability of the whole
-) Pandemonium system is effectively eliminated through the generation
-) and interpretation of {\it confidence\/} and {\it ambiguity\/}
-) measures at every moment during the development of the system.
-) 
-) Two problem domains are used to test and demonstrate various aspects
-) of our architecture.  {\it Reliability\/} and {\it quality\/} measures
-) are defined for systems that only answer part of the time.  Our system
-) achieves better quality values than single networks of larger size for
-) a handwritten digit problem.  When both second and third best answers
-) are accepted, our system is left with only 5\% error on the test set,
-) 2.1\% better than the best single net.  It is also shown how the
-) system can elegantly learn to handle garbage patterns.  With the
-) parity problem it is demonstrated how complexity of problems may be
-) decomposed automatically by the system, through solving it with
-) networks of size smaller than a single net is required to be.  Even
-) when the system does not find a solution to the parity problem,
-) because networks of too small a size are used, the reliability remains
-) around 99--100\%.
-) 
-) Our Pandemonium architecture gives more power and flexibility to the
-) higher levels of a large hybrid system than a single net system can,
-) offering useful information for higher-level feedback loops, through
-) which reliability of answers may be intelligently traded for less
-) reliable but important ``intuitional'' answers.  In providing weighted
-) alternatives and possible generalizations, this architecture gives the
-) best possible service to the larger system of which it will form part.
-) 
-) Keywords:  Reflective architecture, Pandemonium, task decomposition,
-) 	   confidence, reliability. 
-) ********************************************************************
-) 
-) 
-) ----------------------------------------------------------------
-) FTP INSTRUCTIONS
-) 
-)      unix% ftp archive.cis.ohio-state.edu (or 128.146.8.52)
-)      Name: anonymous
-)      Password: neuron
-)      ftp> cd pub/neuroprose
-)      ftp> binary
-)      ftp> get smieja.reflect.ps.Z
-)      ftp> bye
-)      unix% zcat smieja.reflect.ps.Z | lpr
-) (or whatever *you* do to print a compressed PostScript file)
-) ----------------------------------------------------------------
-) 

Apparently the original format was such that it was not possible to
print out on American-sized paper.  Therefore I have changed the
format and re-inserted the file smieja.reflect.ps.Z into the
neuroprose archive.  It should be all on the sheet now.  Instructions
as before.

	-Frank Smieja