Reconstruction Georgios Bardis Vassilios Golfinopoulos Georgios Miaoulis Dimitri Plemenos Dept. of Informatics Dept. of Informatics Dept. of Informatics Laboratoire XLIM TEI of Athens TEI of Athens TEI of Athens Universit? de Limoges gbardis@teiath.gr golfinopoulos@cs.teiath.gr gmiaoul@teiath.gr plemenos@unilim.fr Abstract Declarative Modeling methodologies offer the designer the ability to describe a scene using abstract terms instead of precise geometric elements and properties. The price for this convenience is a large number of compliant geometric models, only a small subset of which is usually of practical interest for the designer. The task of solution evaluation can be tedious and time-consuming whereas the qualities that make these solutions stand out are not always straightforward. In the current work we outline the integration of a machine learning component, trained by user-approved solutions of previous descriptions, with a reconstruction component, able to discover relations and properties implied by the best solutions, into a unique module for description adaptation according to user preferences.