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From: loren@tristan.llnl.gov (Loren Petrich)
Newsgroups: comp.ai
Subject: What Has Traditional AI Accomplished?
Message-ID: <69367@lll-winken.LLNL.GOV>
Date: 6 Oct 90 10:43:45 GMT
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Organization: Lawrence Livermore National Laboratory
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Originator: loren@tristan


	I know that this question may well start a big flame war, but
I would like an idea of exactly what traditional AI has accomplished.
The impression I get is that the main success of traditional AI is in
designing "expert systems", concerning which one has to laboriously
set up an expert system by specifying what may be a large number of
decision rules for the problem one wants to solve. Judging from what I
have read of expert systems, that can be a very difficult and
time-consuming task. And expert system software still does not seem
exactly accessible.

	There is only one exception that I know of, and that is for
computer algebra systems. There, for the most part, the decision rules
are already known quantities, some having been known for centuries.
And most of them are relatively straightforward and unambiguous, thus
relatively easy to implement on a computer. To use some of my own work
as an example, I myself have used computer algebra systems many times
for problems that sometimes require lengthy algebraic manipulations.
We might count computer algebra as a success for traditional AI.

	Is it fair to say that computer algebra is the only
application of traditional AI that has had any widespread use?

	I am not trying to pick on the AI field, but I really think
that it has not come very far over the decades that it has been in
existence.

$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$
Loren Petrich, the Master Blaster: loren@sunlight.llnl.gov

Since this nodename is not widely known, you may have to try:

loren%sunlight.llnl.gov@star.stanford.edu