🚀 go-pugleaf

From: "Ethan Scarl" <unido!uunet!atc.boeing.com!ethan>
To: mod-ki@unido.uucp
Date: Mon, 20 Mar 89 17:43:17 pst
Subject: Re: WORKSHOP ON MODEL BASED DIAGNOSIS (mod-ki message 0216)

mod-ki message 0229
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[Pat Hayes hatte die Autoren der o.g. Mail oeffentlich gefragt,  ob  ein
Begriff MBR (Model Based Reasoning) nicht ueberfluessig  und  verwirrend
sei - seiner Ansicht nach sei das praktisch synonym mit  der  Artificial
Intelligence. Hier die Antwort der "Erfinder". Ich persoenlich halte die
Begriffsinflation in der AI (auch der deutschen)  fuer  wissenschaftlich
verheerend, man schlage dazu in der Bibel das Kapitel ueber  die  Folgen
der babylonischen Sprachverwirrung nach. Lasst uns alle wieder gemeinsam
KI machen und  nicht  Dutzende  von  provinziellen  und  ausschliesslich
karrieremotivierten XYZ-ismen! hwh]

Pat,

Your note indicating confusion about the scope of model-based  reasoning
(MBR) suggests that  the  area  seems  undefined  and  abitrarily  wide,
encompassing all of AI.  This seems  odd.  For  example, the  first  two
issues noted in the Call for Papers are:

* What inference mechanisms take best advantage of  explicit  structural
  and behavioral representations?
* Conversely, what do these inference mechanisms require of a structural
  representation or a behavioral simulation?

It is not easy to see how these (or the others) are confusable with  all
of AI.

Certainly, MBR may profitably use:
>> mechanical  inference,   qualitative   reasoning,   reasoning   under
   uncertainty,
>> machine  perception  and  temporal  knowledge  representation,  among
   others.

But "to use" does not imply  "to encompass,"  or  "to  be  no  different
from." MBR is a subfield of AI and has interconnections like  all  other
subfields. The boundaries may not always be razor sharp, but  that  does
not imply that MBR is indistinguishable from other subfields, much  less
from all of AI.

If the name itself is a source  of  confusion,  then  take  "Model-Based
Reasoning" to abbreviate  lengthier  phrases  such  as  "Reasoning  from
Models of Structure and Behavior."

In diagnosis, the core idea used in MBR is to compare predictions from a
simulation model of a device with the behavior of the  physical  device.
Discrepancies  are  taken  to  indicate  device  malfunction.  A  common
computational technique is to use dependency traces  in  the  simulation
model to track down possibly malfunctioning components and  to  generate
behavioral hypotheses to render the simulation's predictions  compatible
with observations. One interesting consequence of this approach is  that
a malfunction is indicated by anything  except  the  expected  behavior;
this is quite different from traditional fault modeling, which  requires
a set of pre-specified faults.

Simulation models have been used previously, of course;  the  ones  used
here are constrained by the desire to trace  dependencies  and  generate
behavioral hypotheses.  This in turn  encourages  building  models  that
structurally mimic the device and the constraints  upon  its  operation.
E.g., each component of the model corresponds  to  a  component  of  the
actual device. A component's behavior is modeled as a transfer function, 
whose result is passed to components structurally connected to it.

This is markedly different  from  modeling  systems  using  differential
equations. It is also clearly distinguished from  associative  reasoning
of the sort typically captured in  production  rules,  and  avoids  some
serious   difficulties   encountered   by  those  approaches  (e.g.,  in
discovering and operating with untrustworthy sensory data).

Beyond   diagnosis,   the  approach  has  been  applied  to:  generating
diagnostics and distinguishing tests, plan debugging, design, and theory 
debugging (supposing that the model  is  wrong  when  device  and  model
differ).

As for references, work under this general rubric has been going on  for
almost a decade, and there are several landmark papers;  an  easy  place
to start is from Davis and Hamscher's survey in Shrobe's  "Exploring AI"
(Morgan Kaufmann, San Mateo, 1988) collection of survey talks from AAAI.

Ethan Scarl                                                  Randy Davis
Walter Hamscher                                              Dan Dvorak

- on behalf of the MBR Workshop program committee
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