9. *Mining and Filtering.* A value is given to the support threshold σs and the mining step outputs a set of variations. A filter retains only the variations that correspond to substitutions applicable to modify Sol(pb).

10. *Solution Test and Validation.* The user selects the learned variation
∆ = {oil=, peanut oil-, olive oil+} from the result set. ∆ is interpreted as the substitution Σ = peanut oil olive oil, which suggests to replace peanut oil by olive oil in the retrieved recipe. The adaptation path AP = Σ ◦
Σ2 is computed and the repaired solution Sol g(tgt) is presented to the user for validation. The user is satisfied with the corrected solution Sol g(tgt), so the reformulation (∅ ¬ peanut oil, peanut oil olive oil) is added to the adaptation knowledge base AKB.

## 6 Discussion And Related Work

AK acquisition is a difficult task that is recognized to be a major bottleneck for CBR system designers due to the high knowledge-engineering costs it generates. To overcome these knowledge-engineering costs, a few approaches (e.g.,
[5,9,11]) have applied machine learning techniques to learn AK offline from differences between cases of the case base. In [11], a set of pairs of source cases is selected from the case base and each selected pair of source cases is considered as a specific adaptation rule. The featural differences between problems constitute the antecedent part of the rule and the featural differences between solutions constitute the consequent part. Michalski's closing interval rule algorithm is then applied to generalize adaptation rule antecedents. In [9], adaptation knowledge takes the form of a set of adaptation cases. Each adaptation case associates an adaptation action to a representation of the differences between the two source problems. Machine learning algorithms like C4.5 or RISE are applied to learn generalized adaptation knowledge from these adaptation cases in order to improve the system's case-based adaptation procedure.

When applying machine learning techniques to learn adaptation knowledge from differences between cases, one main challenge concerns the choice of the training set: which cases are worth comparing? Arguing that (1) the size of the training set should be reduced to minimize the cost of the adaptation rule generation process and that (2) the source cases that are worth comparing should be the ones that are more similar, only the pairs of source cases that were judged to be similar according to a given similarity measure are selected in [9] and [11]. However, committing to a particular similarity measure might be somewhat arbitrary. Therefore, in [5], the authors decided to include in the training set all the pairs of distinct source cases of the case base. This paper introduces a third approach: the choice of the training set is determined interactively and according to the problem-solving context, taking advantage of the fact that the AK discovery process is triggered on-line. This approach appears to be very promising since the learning algorithm can be parametrized in order to learn only the knowledge that is needed to solve the target problem.