Notice: The reproducibility variables underlying each score are classified using an automated LLM-based pipeline, validated against a manually labeled dataset. LLM-based classification introduces uncertainty and potential bias; scores should be interpreted as estimates. Full accuracy metrics and methodology are described in Coakley et alK. L. Coakley, T. Snelleman, H. Hoos, and O. E. Gundersen, "The embrace of open science: An analysis of a decade of AI research and 56 800 conference papers," Under Review, 2026..
Conditional Independence for Iterated Belief Revision
Authors: Gabriele Kern-Isberner, Jesse Heyninck, Christoph Beierle
IJCAI 2022 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Theoretical | In this paper, we deο¬ne conditional independence as a semantic property of epistemic states and present axioms for iterated belief revision operators to obey conditional independence in general. We show that c-revisions for ranking functions satisfy these axioms, and exploit the relevance of these results for iterated belief revision in general. |
| Researcher Affiliation | Academia | 1TU Dortmund, Germany 2Fern Universit at in Hagen, Germany 3Vrije Universiteit Brussel, Belgium 4University of Cape Town and CAIR, South-Africa |
| Pseudocode | No | The paper focuses on theoretical definitions, propositions, and proofs with illustrative examples, but it does not include any pseudocode or algorithm blocks. |
| Open Source Code | No | The paper does not provide any information or links regarding open-source code for the described methodology. |
| Open Datasets | No | The paper is theoretical and uses abstract examples (e.g., Example 1, 2, 5, 6, 7) involving propositional languages and interpretations, rather than real-world datasets for empirical training or evaluation. |
| Dataset Splits | No | The paper is theoretical and does not involve empirical experiments with dataset splits (training, validation, or test sets). |
| Hardware Specification | No | The paper is theoretical and does not describe any specific hardware used for experiments. |
| Software Dependencies | No | The paper is theoretical and does not list any specific software dependencies with version numbers. |
| Experiment Setup | No | The paper is theoretical and does not describe an experimental setup, hyperparameters, or system-level training settings. |