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From: sticklen@cps.msu.edu (Jon Sticklen)
Newsgroups: comp.ai
Subject: Re: Data Interpretation - Diagnosis expert Task
Message-ID: <1990Nov9.180734.21527@msuinfo.cl.msu.edu>
Date: 9 Nov 90 18:07:34 GMT
References: <85816@tut.cis.ohio-state.edu>
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Organization: Michigan State University
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From article <85816@tut.cis.ohio-state.edu>, by byland@iris.cis.ohio-state.edu (Tom Bylander):
> In article <1990Nov6.184658.29097@asterix.drev.dnd.ca> jberger@asterix.drev.dnd.ca (Jean Berger) writes:
>>
>>	Would someone help me in clarifying in an unambiguous manner
>>	the real difference between two kind of expert tasks namely:
>>
>>	- Data Interpretation and
>>	- Diagnosis
> 


I think that Tom Bylander captured most of what I would say to this
question too. But I would add one other discriminant between "data
interpretation" and "diagnosis." 

Diagnosis should not be considered as an isolated task. The bottom
line when we do diagnostic problem solving is not that we want to
find out "what is wrong" but rather that we want to "fix what is wrong."
Diagnosis is problem solving aimed at characterizing what is wrong, but
the output of diagnositic problem solving is then used to fix what is 
wrong. If we are doing classification-based diagnosis, then the diagnositic
categories for any real world problem will surely have therapy utility.
In fact, that is what diagnostic categories are - equivalence classes
which map situations into appropriate therapies.

So at root I agree with Bylander that diagnosis is a subset of data
interpretation. What I am adding is why its a subset. And that why is
that the output of diagnostic problem solving must have utility for
fixing something that is broken.

	---jon---


Jon Sticklen
AI/KBS Lab - CPS Dept
Michigan State University