Conditional Independence for Iterated Belief Revision
Authors: Gabriele Kern-Isberner, Jesse Heyninck, Christoph Beierle
IJCAI 2022 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Theoretical | In this paper, we define 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. |