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.
Sign-in/Register to access full text options 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callDisclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
