Imprecise Belief Fusion Facing a DST benchmark problem

2024-08-16 · Francisco Aragão, João Alcântara

When we merge information in Dempster-Shafer Theory (DST), we are faced with anomalous behavior: agents with equal expertise and credibility can have their opinion disregarded after resorting to the belief combination rule of this theory. This problem is interesting because belief fusion is an inherent part of dealing with situations where available information is imprecise, as often occurs in Artificial Intelligence. We managed to identify an isomorphism betwin the DST formal apparatus into that of a Probabilistic Logic. Thus, we solved the problematic inputs affair by replacing the DST combination rule with a new fusion process aiming at eliminating anomalies proposed by that rule. We apply the new fusion method to the DST paradox Problem.

📄 PDF Abstract BibTeX arXiv:2408.08928

Code (0)

등록된 구현이 없습니다.

Methods 이 논문이 사용한 방법론

DST Dynamic sparse training methods train neural networks in a sparse manner, starting with an initial sparse mask, and periodically updating the mask based on some criteria.

Propagation of~Interval Belief Structures and~Imprecise Copulas for~Neural Network Verification

2026-06-29 · Francesc Pifarre-Esquerda, Eric Goubault, Sylvie Putot arxiv

Quantitative verification of neural networks requires reasoning about probabilities under substantial uncertainty in both input distributions and their dependence structure. In realistic settings, this information is oft…

Revealed Incomplete Preferences

2022-05-17 · Kirby Nielsen, Luca Rigotti

We elicit incomplete preferences over monetary gambles with subjective uncertainty. Subjects rank gambles, and these rankings are used to estimate preferences; payments are based on estimated preferences. About 40\% of s…

Medical Image Segmentation with Belief Function Theory and Deep Learning

2023-09-12 · Ling Huang

Deep learning has shown promising contributions in medical image segmentation with powerful learning and feature representation abilities. However, it has limitations for reasoning with and combining imperfect (imprecise…

Deep Learning Image Segmentation Medical Image Segmentation Segmentation+3

Belief Base Revision for Further Improvement of Unified Answer Set Programming

2020-02-27 · Kumar Sankar Ray, Sandip Paul, Diganta Saha

A belief base revision is developed. The belief base is represented using Unified Answer Set Programs which is capable of representing imprecise and uncertain information and perform nonomonotonic reasoning with them. Th…

Qualitative Possibilistic Mixed-Observable MDPs

2013-09-26 · Nicolas Drougard, Florent Teichteil-Konigsbuch, Jean-Loup Farges, Didier Dubois

Possibilistic and qualitative POMDPs (pi-POMDPs) are counterparts of POMDPs used to model situations where the agent's initial belief or observation probabilities are imprecise due to lack of past experiences or insuffic…