# Opportunistic Adaptation Knowledge Discovery

Fadi Badra1, Am´elie Cordier2, and Jean Lieber1 1 LORIA (CNRS, INRIA, Nancy Universities)
BP 239, 54506 Vandœuvre-l`es-Nancy, France
{badra,lieber}@loria.fr 2 LIRIS CNRS UMR 5202, Universit´e Lyon 1, INSA Lyon, Universit´e Lyon 2, ECL
43, bd du 11 novembre 1918, Villeurbanne, France Amelie.Cordier@liris.cnrs.fr Abstract. Adaptation has long been considered as the Achilles' heel of case-based reasoning since it requires some domain-specific knowledge that is difficult to acquire. In this paper, two strategies are combined in order to reduce the knowledge engineering cost induced by the adaptation knowledge (AK) acquisition task: AK is learned from the case base by the means of knowledge discovery techniques, and the AK acquisition sessions are opportunistically triggered, i.e., at problem-solving time.

## 1 Introduction

Case-based reasoning (CBR [6]) is a reasoning paradigm based on the reuse of previous problem-solving experiences, called cases. A CBR system often has profit of a retrieval procedure, selecting in a case base a source case similar to the target problem, and an adaptation procedure, that adapts the retrieved source case to the specificity of the target problem. The adaptation procedure depends on domain-dependent adaptation knowledge (AK, in the following). Acquiring AK can be done from experts or by using machine learning techniques. An intermediate approach is knowledge discovery (KD) that combines efficient learning algorithms with human-machine interaction.

Most of previous AK acquisition strategies are off-line: they are disconnected from the use of the CBR system. By contrast, recent work aims at integrating AK acquisition from experts to specific reasoning sessions: this *opportunistic* AK acquisition takes advantage of the problem-solving context. This paper presents an approach to AK discovery that is opportunistic: the KD is triggered at problem-solving time.

The paper is organized as follows. Section 2 introduces some basic notions and notations about CBR. Section 3 presents the CBR system Taaable, which constitutes the application context of the study, and motivates the need for adaptation knowledge acquisition in this application context. Section 4 presents the proposed opportunistic and interactive AK discovery method. In Sect. 5, this method is applied to acquire adaptation knowledge in the context of the Taaable system. Section 6 discusses this approach and situates it among related work. Section 7 concludes and presents some future work.

arXiv:0912.0132v1 [cs.AI] 1 Dec 2009