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Cases were initially
retrieved on the basis of a static similarity metric
which takes into account the goals
that are covered by the case
as well as all of their relevant initial state conditions
Prior studies show it to be a
reasonably effective metric.
In learning mode,
cases were also retrieved on the same basis.
However, in this mode, the failure reasons
attached to the case were used to censor its retrieval.
Each time that a case was retrieved in learning mode,
these failure conditions
were also tested. If each failure reason was not satisfied
in the new problem specification, the retrieval mechanism returned the
case for replay. If, on the other hand, a failure reason
was found to be true in the new problem context,
then the case that repaired the failure was retrieved.
Following retrieval, the problem was solved both by replay of
the retrieved case as well as by planning from scratch.