SEQ@dlgm.daresbury.ac.uk (DARESBURY SEQMAIL) (05/25/89)
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Date: Thu, 25 May 89 15:05 GMT
From: Dan Weld <weld@edu.washington.cs.nooksack>
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Message-Id: <25 MAY 1989 15:05:41ooksack.cs.washington.edu>
To: overt@com.unisys.prc.burdvax
Cc: bio-matrix-arpanet@net.bio.net
In-Reply-To: overt@prc.unisys.com's message of Wed, 24 May 89 15:02:11 -0400
<8905241902.AA13824@caesar>
Subject: qualitative models in biology
Sender: weld%edu.washington.cs.nooksack@net.bio.net
1. Computational tractability. Can you give an example of a
qualitative reasoning system that has solved a serious problem for
which a quantitative model exists but is computationally intractable?
This one's not really implemented but gives one a feel for the rationale:
Suppose you drop a cup of water, what's going to happen? Fluid hydronamics
has a whole set of quantitative models that you might try to apply (Navier-
Stokes equation, etc), but for the cocktail party domain, it is enough to
know that the liquid will leave the cup, some will splash and some will
spread along the floor. (Of course, it is nontrivial to create a reasonable
qualitative model of this domain, as Hayes showed, but a qualitative model
is clearly appropriate.)
2. > A quantitative model requires quantitative input. You can't use it if
> you don't have it.
This one I have no argument with and is one of the main reasons I
think qualitative models are important in biology. But again, I see
them as intermediates until quantitative models are feasible.
For the example above, a quantitative model will never be feasible. What
will you do when you drop the cup if you don't know the exact volume of
liquid, its precise temperature (which influences viscosity), and the
gravitational constant for your current location? Fluid systems are likely
chaotic so very precise numbers are necessary here.
Actually, what I really like are combined qualitative/quantitative
models a la Erik Sandewall.
This is interesting stuff, but has a number of problems. Basically, his
paper suggests two things: integrating discrete and continuous
representations (which I agree is long overdue) and abandoning discrete
qualitative representations in exchange for quantitative analysis. This
last point has several problems (which he admits). The most important is
that it is not implemented and there is no way I can see to implement it.
Nonlinear systems don't have closed form solutions. The earliest work on
qualitative physics (de Kleer's SYN program) took exactly Sandewall's
approach and tried to analyze electrical circuits by writing out Kirchoff's
laws for all nodes and all loops and plugging them into the MACSYMA
algebraic system. MACSYMA died. Yet novice EE students could analyze the
circuits just fine. The trick is using qualitative reasoning when
appropriate to focus the use of quantitative techniques and applying
simplifying assumptions to abstract one's model. this is a huge area of
research that I can't summarize here.
3. > Qualitative models support explanatation of reasoning so people can
> understand why a program reached the conclusions it did.
On the surface, I like the idea of keeping track of a chain of
reaoning. However, I'm a more than a little suspicious that this
feature will not prove useful when trying to model a truly complex
system with lots of feedback loops (non-linear too), ie most of
biology.
It's true that this is a difficult problem, but there is some really
interesting research on dynamic construction of abstract models. See my
work on Aggregation (AI journal October '86) in a biochemical domain or the
work on PROMPT by Addanki and the folks at IBM Yorktown applied to
mechanical systems. In fact there is a whole chapter on this in our book,
and a number of people are doing excellent research in this area.
> Increased confidence.
Here we part company. Qualitative models are very weak approximations
to reality and so would seem to be the least likely to give correct
predictions or explanations. How does someone even go about validating
a qualitative model? There are fairly standard approaches for
validating numerical models, but I haven't seen anyone address this
issue for qualitative models. Any references?
there certainly are some techniques. For example, Kuipers' has a soundness
proof for QSIM (AI journal Sept 86) but no completeness result. In many
respects the same approaches that work for standard simulation models work
for qualitative models (and again qualitative models are not the only kind
that are going to be of interest to Biologists); see my paper in this
summer's qualitative physics workshop. And of course, this is an area for
more research too.
Dan
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