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From: tgd@orstcs.UUCP (tgd)
Newsgroups: net.ai
Subject: Re: taxonomizing in AI: useless, harmful
Message-ID: <14100007@orstcs.UUCP>
Date: Wed, 19-Feb-86 12:09:00 EST
Article-I.D.: orstcs.14100007
Posted: Wed Feb 19 12:09:00 1986
Date-Received: Mon, 24-Feb-86 21:38:56 EST
References: <-36000@iuvax.UUCP>
Organization: Oregon State University - Corvallis, OR
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Nf-ID: #R:iuvax:3600038:orstcs:14100007:000:1217
Nf-From: orstcs!tgd    Feb 19 09:09:00 1986

Taxonomic reasoning is a weak, but important form of plausible reasoning.
It makes no difference whether it is applied to man-made or naturally
occurring phenomena.  The debate on the status of artificial intelligence
programs (and methods) as objects for empirical study has been going on
since the field began.  I assume you are familiar with the arguments put
forth by Simon in his book Sciences of the Artificial.  Consider the case of
the steam engine and the rise of thermodynamics.  After many failed attempts
to improve the efficiency of the steam engine, people began to look for 
an explanation, and the result is one of the deepest theories of modern
science.  

I hope that a similar process is occurring in artificial intelligence.  By
analyzing our failures and successes, we can attempt to find a deeper theory
that explains them.  The efforts by Michalski and others (including myself)
to develop a taxonomy of machine learning programs is viewed by me, at
least, not as an end in itself, but as a first step toward understanding the
machine learning problem at a deeper level. 

Tom Dietterich
Department of Computer Science
Oregon State University
Corvallis, OR 97331
dietterich@oregon-state.csnet