Belief Functions: Theory and Applications - 8th International Conference: BELIEF 2024

The theory of belief functions, introduced by Arthur P. Dempster and Glenn
Shafer in the 1960’s and 1970’s, is now well established as a general frame-
work for reasoning under uncertainty, with well-understood connections to other
frameworks such as random sets, possibility and imprecise probability theories.
In the last 40 years, it has been applied to a wide range of problems in statistics,
computer science and engineering. In particular, in recent years, it has inspired
many important contributions to statistical inference and machine learning.
The series of biennial International Conferences on Belief Functions (BE-
LIEF), sponsored by the Belief Functions and Applications Society (BFAS), are
dedicated to the confrontation of ideas, the reporting of recent achievements,
and the presentation of the wide range of applications of this theory. The first
edition of this conference series was held in Brest, France, in 2010. Later editions
were held in Compi`egne, France in 2012, Oxford, UK in 2014, Prague, Czech Re-
public in 2016, again in Compi`egne, France in 2018, Shanghai, China in 2021
and in Paris, France, in 2022.
The 8th International Conference on Belief Functions (BELIEF 2024) was
held in Belfast, United Kingdom, September 2-4 2024. This volume contains
the proceedings of BELIEF 2024 composed of 30 accepted submissions, each
reviewed by either two or three peers in a single-blind review process. Original
contributions address theoretical issues including continuous belief functions,
random fuzzy sets, measures of uncertainty and conflict, as well as methods
for solving various problems in machine learning, information fusion, statistical
inference and optimization.
We would like to thank all the people who made this volume and this confer-
ence possible: all contributing authors, the organizers, and the Program Commit-
tee members. We are especially grateful to our three invited speakers, Prof. Zhi-
Hua Zhou (Nanjing University, China), for his talk A Preliminary Exploration
to Learnware, Prof. Prakash P. Shenoy (University of Kansas, United States),
for his talk Knowing What You Don’t Know: Making Inferences in Incomplete
Bayesian Networks and Prof. Fr´ed´eric Pichon (Artois University, France), for
his talk Reliability and dependence in information fusion. We would also like
to thank the Belief Functions and Applications Society and the School of Com-
puting at Ulster University, UK for sponsoring the event, the Ulster University
for hosting the event, the International Journal of Approximate Reasoning and
Elsevier. Furthermore, we would like to thank the editors of the Springer-Verlag
series Lecture Notes in Artificial Intelligence (LNCS/LNAI) and Springer-Verlag
for their dedication to the production of this volume.