From: sjameson@fergie.dnet.ge.com (Stephen M Jameson)
Newsgroups: comp.ai
Subject: Re: Case-Based Reasoning as Problem Solving
Date: 22 Jul 92 12:56:52 GMT
Reply-To: sjameson@atl.ge.com
Organization: General Electric Advanced Technology Labs

In article <1992Jul15.212602.9280@msuinfo.cl.msu.edu> wallingf@cps.msu.edu (Eugene Wallingford) writes:

>     Our disagreement concerns usage of the term "problem solving" with
>     respect to case-based reasoning.  I have made the statement that
>     case-based reasoning can be viewed as a general methodology for
>     problem solving.  (Of course, it can also be viewed more broadly.)
>     My colleague objects to this characterization on the grounds that
>     the use of cases from a memory -- particularly a memory that is
>     indexed automatically by the CBR system as it goes about its
>     business -- may not reflect knowledge of the domain.  

If the cases don't reflect knowledge of the domain, then what do they reflect?
I think your colleague's argument may stem from the old (relatively) dispute
about deep versus shallow knowledge.  Deep knowledge consists of underlying
knowledge about how the domain works (such as the basic theories of physics),
while shallow knowledge consists of sets of examples which are believed to
characterize the behavior of the domain (such as a set of examples from a
high school physics text).  Many people are uncomfortable with the claim that
by knowing a set of examples of how the domain operates, you really "know" the
domain, and in general they are justified.  However, in many domains, it is
either not possible to characterize the deep knowledge in a computationally
usable way, or the computation required to make use of is it in most cases
excessive, so one must resort to the use of shallow knowledge, with occasional
use of deep knowledge to "interpolate" to fill in the gaps between the known
cases (see the work of Leake).


>     That is,
>     the indexing of the memory may seem "opaque" to a knowledgeable
>     problem solver because such indexing may not conform to some
>     knowledge that the "expert" using in reaching a solution.

This argument has merit only in the cases where there is no (practical)
objective means of determining the correctness of the solution developed by the
CBR system.  This is the case in many domains, medicine in particular, where
the only practical judge of the correctness is comparison with the opinions of
medical "experts" [of course, you could proceed on the basis of the system's
recommendations and see how well it works; your lawyer will love you for it].
On the other hand, in a domain where the cost of occasionally determining
"ground truth" for comparison, or of accepting and investigating occasional
wrong answers is not inordinate, there is a practical objective means of
verifying the correctness of the problem-solver, and it becomes less important
that the CBR system solve problems the same way human experts do.  

In general, however, the reason why CBR has been pushed as a problem-solving
methodology (yes, that is what it is) is that many people (such as Schank)
believe that it conforms closely to the way humans actually solve many types of
problems.  There is considerable evidence (see the work of Koton, for example)
that this is true in the medical domain, and it is quite clearly true in the
legal domain (see Rissland and Ashley), and so CBR seems an appropriate
paradigm for producing automated problem-solvers in these areas.

Steve Jameson
sjameson@fergie.dnet.ge.com       
General Electric Aerospace
Advanced Technology Laboratories    
Moorestown, New Jersey              
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