{C}ase-based {R}easoning in {K}nowledge {D}iscovery and {D}ata {M}ining

Abstract

This thesis presentssomecontributions in three research domains : case-based reasoning, knowledge discovery and knowledge representation. Case-based reasoning consists in solving new problems by reusing a set of previous problem-solving experiences, called cases. In this thesis, a language is introduced to represent variations between cases.We first show how this language can be used to represent adaptation knowledge and to model the adaptation phase in case-based reasoning. This language is then applied to the task of adaptation knowledge learn- ing. A knowledge discovery process, called CabamakA, is proposed, that learns adaptation knowledge by generalization from a representation of variations between cases. A discussion follows on how to make this knowledge discovery process operational in a knowledge ac- quisition process. The discussion leads to the proposition of a new approach for adaptation knowledge acquisition, in which the knowledge discovery process is triggered in an oppor- tunistic manner at problem-solving time. The concepts introduced in the thesis are illustrated in the cooking domain through their application in the case-based reasoning system Taaable, that constitutes the application domain of the study.