Basic Belief Assignment

Basic belief assignment (BBA) is a framework for representing uncertainty, particularly useful in situations where probabilities are difficult to assign. Current research focuses on improving the efficiency and interpretability of BBA models, including developing new algorithms for combining belief functions and integrating BBA with other uncertainty representations like probability and possibility theory. This work aims to enhance the application of BBA in areas such as artificial intelligence, particularly in decision-making under uncertainty, and to address challenges related to computational complexity and the sensitivity of combination rules to input order. The ultimate goal is to create more robust and reliable systems capable of handling uncertainty in complex real-world scenarios.