INTRODUCTION
Case-based reasoning system is a
recent approach to knowledge-based problem solving and decision support: A new
problem is solved by remembering a previous similar situation and by reusing
information and knowledge of that situation. Let us illustrate this by looking
at some typical problem solving situations:
A physician is
examining a patient in his office. He gets a reminding to a patient that he
treated two weeks ago. Assuming that the reminding was caused by a similarity
of important symptoms, the physician uses the diagnosis and treatment of the
previous patient to determine the disease and treatment for the patient in
front of him.
A financial
consultant working on a difficult credit decision task uses a reminding to a
previous case
, which involved a
company in similar trouble as the current one, to recommend that the loan
application should be refused.
A drilling engineer has experienced
several dramatic blow out situations. He is quickly reminded of one of these
situations when the combination of critical measurements during drilling
matches those of a blow out case. In particular, he may get a reminding to a
mistake he made during a previous blow-out, and use this to avoid repeating the
error once again.
Reasoning by reusing past cases is a
powerful and frequently applied way to solve problems for humans. This claim is
supported by results from cognitive psychological research, and a part of the
foundation for the case-based approach is its psychological plausibility
[Ross-89]. Case-based reasoning is a problem solving paradigm that in many
respects is fundamentally different from other major AI approaches. Instead of
relying solely on general knowledge of a problem domain, or making associations
along generalized relaÂtionships between problem descriptors and conclusions,
CBR is able to utilize the
specific knowledge of previously experienced, concrete problem
situations (cases). A new problem is solved by finding a similar past case, and
reusing it in the new problem situation. A second important difference is that
CBR also is an approach to incremental, sustained learning, since a new
experience is retained each time a problem has been solved, making it
immediately available for future problems. Case-based reasoning can be
considered a form of analogical reasoning, where the analogs typically are
within the same application domain. However, as I will get back to later, the
main body of research on analogical reasoning has a different focus, namely analogies
across domains.
In
CBR terminology, a
case usually denotes a problem situation including
its interpretation, solution, and possible annotations. A case is previously
experienced situation, which has been captured and learned in such way that it
can be reused in the solving of future problems.
CBR
is a combined approach to problem solving and machine learning, and a strong
driving force behind case-based methods has come from the machine learning
community. This makes CBR methods particularly interesting from a decision
support point of view, since learning abilities in such systems is something
that is urgently needed, but so far missing. Learning in CBR occurs as a
natural by-product of problem solving. When a problem is successfully solved,
the experience is retained in order to solve similar problems in the future.
When an attempt to solve a problem fails, the reason for the failure is
identified and remembered in order to avoid the same mistake in the future.
It
seems clear that human problem solving and learning in general are processes
that involve the representation and utilization of several types of knowledge,
and the combination of several reasoning methods. If cognitive plausibility is
a guiding principle, an architecture for knowledge-based systems where the
reuse of cases is at the centre, should also incorporate other and more general
types of knowledge in one form or another. This is an issue of current concern
in CBR research.
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