KR2026Proceedings of the 23rd International Conference on Principles of Knowledge Representation and ReasoningProceedings of the 23rd International Conference on Principles of Knowledge Representation and Reasoning

Lisbon, Portugal. July 20-23, 2026.

Edited by
  1. Renata Wassermann
  2. Marie-Laure Mugnier
  3. Franz Baader

ISSN: 2334-1033
ISBN: 978-1-956792-18-8

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Published by

Copyright © 2026 International Joint Conferences on Artificial Intelligence Organization

Truth-Tracking by Iterated Belief Change

  1. Nicolas Schwind(National Institute of Advanced Industrial Science and Technology (AIST), Tokyo, Japan)
  2. Patricia Everaere(Univ. Lille, CNRS, Centrale Lille, UMR 9189 CRIStAL, F-59000 Lille, France)
  3. Sébastien Konieczny(CRIL, CNRS - Université d'Artois, France)
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BibTeX
https://doi.org/10.24963/kr.2026/55

Keywords

  1. null-Iterated Belief Change
  2. null-Improvement Operators
  3. null-Truth Tracking

Abstract

We investigate the truth-tracking performance of iterated belief change operators. In particular, we show that a class of improvement operators is guaranteed to converge to the truth when the input sequence contains sufficiently many correct pieces of information, and we establish a corresponding convergence theorem. We also report experimental results indicating that this convergence typically occurs with relatively short input sequences.