Anticipation de situations dynamiques à partir de scénarios

Abstract

Artificial intelligence can be used to recognise and anticipate dynamic situations. Several computational methods based on mathematical tools already exist, but most of the time their implementation is complex and takes longer execution time. In this article we propose another learning and anticipation method intended to user assistance in dynamic situations. This scenario-based anticipation algorithm is inspired from case-based reasoning. It works with symbolic data and its aim is to make real time predictions. To do so, manipulated knowledge is especially structured to limit our solution's complexity and to facilitate learning and anticipation.