learning algorithms with human-machine interaction. In [5], AK is learned from differences between cases by the means of knowledge discovery techniques. A
set of pairs of sources cases is taken as input of a frequent itemset extraction algorithm, which outputs a set of itemsets. Each of these itemsets can be interpreted as an adaptation rule. This approach of AK learning was motivated by the original idea proposed by Kathleen Hanney and Mark T. Keane in [11], in which the authors suggest that AK may be learned from differences between cases. The main assumption is that the differences that occur between cases in the case base are often representative of differences that will occur between future problems and the case base.

To learn adaptation rules from differences between cases, representing variations between cases is essential. In [2], expressive representation formalisms are proposed and it is shown that defining a partial order on the variation language can help organizing the learned rules by generality.

## 4.2 Opportunistic And Interactive Knowledge Acquisition

Experiential knowledge, or know-how, can often be acquired on-line, when users are using CBR tools. It is the aim of interactive and opportunistic knowledge acquisition strategies to support such an acquisition. In these strategies, the system exploits its interactions with its user to build new pieces of knowledge, to test them and, in case of success, to retain them. Moreover, the knowledge acquisition process is often opportunistic, i.e, triggered by a previous reasoning failure: reasoning failures highlight missing knowledge and thus constitute a guidance for the acquisition process. A major advantage of interactive knowledge acquisition strategies is that they ensure that the user is in a favorable context when he participates to the acquisition process. In [7], a review of interactive and opportunistic knowledge acquisition approaches is proposed, and two strategies are developed. This work illustrates the efficiency of interactive and opportunistic knowledge acquisition approaches to acquire specific knowledge. On the other hand, it shows that such approaches only allow the systems to acquire small pieces of knowledge at a time.

## 4.3 Combining The Two Approaches

When properly used, knowledge discovery techniques may have the strong advantage of automating a part of the knowledge acquisition process. In these approaches, dedicated human-machine interfaces allow the expert, through predefined interactions, to provide feedback on a set of suggestions generated automatically by the system. The role of the expert is thus reduced to the validation of a pre-selected set of knowledge pieces. The acquired knowledge is directly usable by the system, without the need for an additional formalization step. Automatic approaches also benefit from efficient machine learning algorithms that can be applied, as in [2], to learn adaptation rules at different levels of generality. However, these approaches still produce a large number of