The Belief Function Theory represents a new approach devised to model and manage imprecise information in Artificial and Computational Intelligence. The theory of belief functions also referred to as evidence theory or Dempster-Shafer theory, was first introduced by Arthur P. Dempster in the context of statistical inference, and was later developed by Glenn Shafer as a general framework for modeling epistemic uncertainty. These early contributions have been the starting points of many important developments, including the Transferable Belief Model and the Theory of Hints. The theory of belief functions is now well established as a general framework for reasoning with uncertainty, and has well understood connections to other frameworks such as probability, possibility and imprecise probability theories. The field of application of this theory is very large.

The aim of this first spring school is to promote this theory and to introduce interested students and researchers to the basics of Belief Functions, both theoretical and applied. The school is organized into several lectures given by international experts. They will bring both theoretical and practical backgrounds, in a friendly environment favoring interaction between participants. An important part of the time will be devoted to the resolution of proposed exercises concerning applied problems.

This spring school is organized by GIPSA-lab and CNRS and sponsored by …