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Retrieving a case means to start with a (partial) new case, and retrieve
the best matching previous case. It involves the following subtasks:
- Identify features - this may simply be to notice the
feature values for a case, or can be a more complex evaluation which
tries to ``understand'' the problem in a context. This can be
filtering out noisy problem descriptors, infer other relevant problem
features, check whether the feature values make sense in the given
context, or generate expectations of other feature values. The
checking might be done against a knowledge model (cases or general
knowledge) or by asking the user.
- Initially match - usually done in two parts, first an initial
matching process which gives a list of possible candidates, which are then
further examined to select the best. There are three ways of
retrieving a
case or a set of cases: By following direct index pointers from the
problem features, by searching an index structure, or by searching in a model
of domain knowledge. Matching cases can be found by comparing with input
features or input features and features inferred from others using domain
knowledge. The features can be compared
using some similarity measure, which is usually normalized, for
instance to the range [0,1], so that it is easy to compare cases based
on several or all features. The Case-Based reasoner can try to ``understand''
the problem, and use this understanding in comparing. It can also weigh the
input features.
- Select - select a best match from the cases returned by the
initially match. The reasoner tries to explain away non-identical
features. If the match is not good enough, a better one is sought by
using links to closely related cases. The selection process can
generate consequences and expectations from each retrieved case, by
using an internal model or by asking the user.
Next: Reuse
Up: Decomposition of CBR
Previous: Decomposition of CBR
Torgeir Dingsoyr
2/26/1998