Name	Machine learning for model acquisition
Begin	2000
State	ongoing
Description	Model acquisition is an important issue for model-based diagnosis, especially as modeling dynamic systems. We investigate machine learning methods concerning temporal data recorded by sensors or spatial data resulting from simulation processes. Supervised techniques coming from the field of relational learning as well as unsupervised techniques coming from data mining are investigated.
Actions	Chronicle discovery by mining data streams Learning decision-oriented rules from simulation data Mining with poor a priori knowledge about the structure of data