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 modelling 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. It has been applied in diverse areas such as machine learning, information fusion and risk analysis.
The BELIEF conferences, sponsored by the Belief Functions and Applications Society, 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 Compiègne, France in 2012, Oxford, UK in 2014, Prague, Czech Republic in 2016, and again in Compiègne, France in 2018. The Sixth International Conference on Belief Functions (BELIEF 2021) will be located in Shanghai, China, on October 15-19, 2021, together with the 2021 International Conference on Cognitive analytics, Granular computing, and Three-way decisions (CCGT). It will be held both onsite and online due to the COVID-19 situation (see Venue and Registration below for details).
The expected length of papers is no longer than 8 pages, references included, that should present original contributions with significant results. Springer encourages authors to include their ORCIDs in their papers. In addition, the corresponding author of each accepted paper, acting on behalf of all of the authors of that paper, will have to complete and sign a Consent-to-Publish form. The corresponding author signing the copyright form should match the corresponding author marked on the paper. Once the files have been sent to Springer, changes relating to the authorship of the papers cannot be made.
Original contributions are solicited on theoretical aspects including, but not limited to
as well as on applications to various areas including, but not limited to
Authors of selected papers from the BELIEF 2021 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.
The program of this edition of the BELIEF conference will include tutorials from experts on belief functions and their applications.
To accommodate for the uncertainties surrounding travel possibilities due to the COVID-19 pandemic, participants have two options to attend the conference: either online or onsite.
The onsite event will take place at the Gu Cun Park Hotel (No.4788 Hu Tai Road, Baoshan District, Shanghai).
The registration fee for the onsite participants will include the following items:
We expect most keynote talks to be given onsite (and tutorials to be given online).
Online participants will be able to join and participate to the onsite event via an online platform. They benefit from a discounted registration fee, which will include the following items:
|Additional ticket for gala dinner||150||20|
|Additional ticket for welcome reception||130||17|
The onsite and online student registration fees are reduced in comparison to the standard onsite and online registration fees. Furthermore, the Belief functions and Applications Society (BFAS) is offering a number of grants to allow students with limited funding opportunities to present their work at the conference. The grant covers the registration fee (online or onsite fee depending on the chosen mode of participation by the student). Candidates should send the following information: CV, recommendation letter from supervisor and copy of paper accepted at BELIEF 2021 to the secretary of BFAS, Dr. Anne-Laure Jousselme (firstname.lastname@example.org).
Professor Deqiang Han, Xi'an Jiaotong University, China.
Title: Learning-based Modelized Methods for Evidence Combination
Abstract: Evidence combination is typical uncertainty reasoning or information fusion in the theory of belief functions, which combines bodies of evidence stemming from different information sources. In traditional applications of evidence combination (e.g., pattern classification), given a sample, the basic belief assignments (BBAs) of different information sources are generated first, and then they are combined by a rule, e.g., Dempster's rule. We propose a modelized method for evidence combination. By just inputting the sample into the learned model of combination, a “combined” BBA is obtained. That is, it does not need to generate multiple BBAs for each sample for the combination. In our proposed modelized combination, one can generate different combination models with different combination rules. Experimental results and related analyses validate the related rationality and efficiency.
Professor Van Nam Huynh, Japan Advanced Institute of Science and Technology, Japan.
Title: Machine Learning coupled with Evidential Reasoning for User Preference
Abstract: Inferring user preferences from short texts generated by users on social platforms has a variety of applications in web-based decision support systems such as recommender systems and personalized marketing systems. Developing an efficient solution to this problem is still challenging due to difficulty in handling short texts and dynamic change of user preferences over time. In this talk, we will present a novel framework that tackles these challenges by combining advanced Machine Learning techniques for concept learning and Dempster-Shafer theory (DST) for reasoning and fusion to effectively infer user preferences. Two instances of the proposed framework will be demonstrated with experimental results and analysis that show the effectiveness and practicality of the developed methods.
Ass. Professor Chunlai Zhou, Renmin University, China.
Title: Basic Utility Theory for Belief Functions
Abstract: I will talk about a basic utility theory for belief functions which is common ground for different decision theories in Dempster-Shafer theory where the completeness requirement is dropped. The resulting preference relation is represented by subjective expectation of sets of utilities whose ordering is based on an ordering of outcome sets derived from a logical decision theory for complete ignorance. Moreover, we explore the preference aggregation problem within the utility theory and generalize some results by Harsanyi and Mongin to the setting of belief functions.
Professor Zengjing Chen, Shandong University, China.
Title: A Central Limit Theorem for Sets of Probability Measures
Abstract: We prove a central limit theorem for a sequence of random vari- ables whose means are ambiguous and vary in an unstructured way. Their joint distribution is described by a set of measures. The limit is (not the normal distribution and is) defined by a backward stochastic differential equation that can be interpreted as modeling an ambiguous continuous-time random walk.