SUO: Architecture of an intelligent ontology development algorithm

["Richard Cooper" <rich@valutech.com>]

Since many of us seem to agree that a bottom-up
algorithm could be used to produce the axiom 
set of an ontology through situated experience
in the real world, I'm trying to draft some
requirements for this algorithm.  

There is a very suggestive paper at
http://jasss.soc.surrey.ac.uk/6/3/1.html
"Discrete Agent Simulations of the Effect of 
Simple Social Structures on the Benefits of 
Resource Sharing".  

The paper desribes a simulation of agents in an
environment somewhat like early human societies
are thought to have evolved in.  

A similar approach could be used to measure
the success of each strategy on the basis of
how successful agents use that strategy.  In
a simulated environment, instead of a situated
one, its easy to measure behaviors and organize
them according to what works well and what
doesn't.  

So in a situated environment, perhaps the 
algorithm can guess at axioms based on fragments
of previous guesses that were successful.  The
so-called evolutionary algorithms could suggest
requirements for monitoring the algorithm's
behavior in the real world, measuring success
and failure, and buliding a database of experience
for process improvement.  

So it seems to me that the process improvement
concepts should be a top level ontology in an
algorithm that learns still higer level axioms,
while the WordNet concept set provides at least
the words for communicating with real world
people.  

Any thoughts on this subject?

Thanks,
Rich