Scenario-based anticipation for aided navigation

Artificial intelligence may be used to recognize and anticipate dynamic situations. Several computational methods based on mathematical tools already exist, but most of the time their implementation is complex and takes long execution time. In this article we propose another learning and anticipation method to assist a user in dynamic situations. We call it `scenario-based reasoning´ algorithm. It 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.