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From: hendler@dormouse.cs.umd.edu (Jim Hendler)
Newsgroups: comp.ai
Subject: Re: NETL / Scott Fahlman
Message-ID: <32312@mimsy.umd.edu>
Date: 2 Apr 91 02:23:40 GMT
References: <1991Mar28.232208.14004@cs.cmu.edu>
Sender: news@mimsy.umd.edu
Reply-To: hendler@dormouse.cs.umd.edu (Jim Hendler)
Organization: U of Maryland, Dept. of Computer Science, Coll. Pk., MD 20742
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Scott Fahlman writes that little or no follow up work has been done to
NETL on the CM.  He's only partially right, my students and I have
been working on developing a frame-based knowledge rep. language for
the CM.  The inheritance algorithms in PARKA are more sophisticated
than those used by NETL, more information is propagated in the
activation stage, and the language is significantly more ambitious
than NETL was (the PARKA language, when completed, should be close to
equivalent to most of the term subsumption languages being discussed 
these days).  It will also be quite fast, several conference papers
and a forthcoming JPDC article discuss the results in inheritance -
basically we get linear (order of depth of the network) times for
top-down inheritance in multiple inheritance hierarchies.  For
networks having over 30,000 nodes (averaging 8-10 links per node) we
can find all nodes with a given property on time in the order of 1 - 5
seconds (depending on the network topology).  These timings were done
on random networks.  We've also hand crafted a 1000 node network of
facts about US states, animals, and agriculture that we are analyzing
so as to get a better topological analysis to use in the generation of
large random networks.  Technical reports describing both the parallel
implementation and the language design are available.
  -Jim Hendler
   UMCP
p.s. work in PARKA is funded by the office of naval research under
grant N00014-88-K-0560.