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BELIEF - 2026 2026 : 9th International Conference on Belief Functions

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Link: https://www.bfasociety.org/Belief2026
 
When Oct 19, 2026 - Oct 21, 2026
Where Nanjing, China
Submission Deadline Apr 30, 2026
Notification Due Jun 15, 2026
Final Version Due Jun 30, 2026
Categories    artificial intelligence   uncertain reasoning   information fusion   computer science
 

Call For Papers

The 9th International Conference on Belief Functions (BELIEF 2026)

Dates: October 19-21, 2026
Location: Nanjing, Southeast University, China

* April 30, 2026: Paper submission deadline
* June 15, 2026: Author notification
* June 30, 2026: Camera-ready copy due

================================================================

The theory of belief functions, also referred to as evidence theory or Dempster-Shafer theory is a well-established mathematical framework for reasoning and decision-making with uncertainty, with applications to machine learning, statistical inference, information fusion, knowledge representation, risk analysis, etc. It is an extension of probability theory and it has well understood connections with related frameworks such as random sets and imprecise probabilities. Recently, generalised evidence theory, based on random fuzzy sets, has been proposed as a unifying framework encompassing both Dempster-Shafer and possibility theories.

The biennial BELIEF conferences (sponsored by the Belief Functions and Applications Society https://www.bfasociety.org/) are dedicated to the confrontation of ideas, the reporting of recent achievements and the presentation of the wide range of applications of this theory. Previous editions of this conference series were held in Brest, France (2010); Compiègne, France (2012); Oxford, UK (2014); Prague, Czech Republic (2016); Compiègne, France (2018); Shanghai, China (2021); Paris, France (2022), and Belfast, UK (2024).

To support cross-fertilization among researchers working in different subfields of AI and related disciplines, tutorials and special sessions will be proposed, with emphasis on the links between machine learning and uncertain reasoning, including topics such as quantification of prediction uncertainty, fusion rules for ensemble learning, belief propagation in deep neural networks, links with explainable and symbolic AI, etc. Submissions of papers combining several of these topics, or more generally at the intersection between belief functions and other AI methods or uncertainty theories are welcome. We also encourage submissions describing innovative applications to various areas including, but not limited to: energy, environment and climate change; medical diagnosis; risk and reliability analysis; business and economics, vision and image processing, etc.

===========Proceedings===========

Proceedings of the previous editions of BELIEF have been published by Springer-Verlag as volumes of the Lecture Notes in Artificial Intelligence (LNCS/LNAI) series and indexed by: ISI Web of Science; EI Engineering Index; ACM Digital Library; dblp; Google Scholar; IO-Port; MathSciNet; Scopus; Zentralblatt MATH. Publication of the BELIEF 2026 proceedings in the LNCS/LNAI series is awaiting confirmation.

==========IJAR Special issue==================

Authors of selected papers from the BELIEF 2026 conference will be invited to submit extended versions of their papers for possible inclusion in a special issue of the International Journal of Approximate Reasoning.

============BELIEF 2026 Program Committee co-chairs==============

Prof. Zhigang Su (zhigangsu@seu.edu.cn), Southeast University, China
Prof. Thierry Denoeux (tdenoeux@utc.fr), Université de Technologie de Compiègne, France

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