Generalization in automatic learning using aggregation and prolongation
Résumé
This paper is concerned with Automatic Symbolic Learning. More precisely, our aim is to extract rules describing the Input/Output behaviour of a system, from observations which might be incoherent. The presented approach traduces an orientated learning situation, in which the knowledge acquisition (model building) is made through the consideration of a set of examples (the data).