KR2023Proceedings of the 20th International Conference on Principles of Knowledge Representation and ReasoningProceedings of the 20th International Conference on Principles of Knowledge Representation and Reasoning

Rhodes, Greece. September 2-8, 2023.

Edited by
  1. Pierre Marquis,
  2. Tran Cao Son
  3. Gabriele Kern-Isberner

ISSN: 2334-1033
ISBN: 978-1-956792-02-7

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Published by

Copyright © 2023 International Joint Conferences on Artificial Intelligence Organization

Weighted Merging of Propositional Belief Bases

  1. Patricia Everaere(CRIStAL - Université Lille)
  2. Chouaib Fellah(CRIL - Université d'Artois)
  3. Sébastien Konieczny(CRIL - CNRS)
  4. Ramón Pino Pérez(CRIL - Université d'Artois)
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BibTeX
https://doi.org/10.24963/kr.2023/22

Keywords

  1. Belief revision and update, belief merging, information fusion

Abstract

In standard propositional belief merging, one implicit assumption is that all sources have exactly the same importance. But there are many situations where the sources have different importance/reliability/expertise that have to be taken into account in the merging process.

In this work we study the problem of weighted merging operators, which aimed to take these weights into account in a sensible way.

We give a syntactical characterization of these operators, and then we state a representation theorem in terms of plausibility preorders on interpretations.

We also propose a general method to build weighted distance-based merging operators, and provide some concrete examples, using two different weight functions.