Development of algorithms and methods for information fusion enabled decisionsupport

In every situation humans make observations and analyse the observed information, by combining and evaluating the relationships between the observation to understand the situation. Often these observations can be uncertain, redundant, from unknown sources, contradictory, and too many for a human to be able to analyse fast enough and correctly, leading to incorrect assessment of the situation. The proposed research is based on the hypothesis that through application of various probablistic, possibilistic, and other applied mathematics methods it is possile to enhance humans ability to analyse large amounts of redundant, uncertain, sometimes contradictory information, enhancing his/her capability to correctly understant situations. Te proposed research will extend the currently ongoing research on the application of imperfect information representation theories (including belief functions with their link to probability) to enhance situation understanding in a context where information, coming from a broad variety of sources (internet, police, military, coast guard, etc.) is uncertain…….

Faculty Supervisor:

Jiri Patera

Student:

Partner:

OODA Technologies Inc

Discipline:

Mathematics

Sector:

Professional, scientific and technical services

University:

Université de Montréal

Program: