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..
Iterated Belief Base Revision: A Dynamic Epistemic Logic Approach
Authors: Marlo Souza, Álvaro Moreira, Renata Vieira3076-3083
AAAI 2019 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Theoretical | This work investigates how priority graphs, a syntactic representation of preference relations deeply connected to prioritised bases, can be used to characterise belief change operators, focusing on well-known postulates of Iterated Belief Change. We provide syntactic representations of belief change operators in a dynamic context, as well as new negative results regarding the possibility of representing an iterated belief revision operation using transformations on priority graphs. |
| Researcher Affiliation | Academia | Marlo Souza,1 Alvaro Moreira,2 Renata Vieira3 1Department of Computer Science, UFBA, Salvador, Brazil 2Institute of Informatics, UFRGS, Porto Alegre, Brazil 3Polytechnic School, PUCRS, Porto Alegre, Brazil |
| Pseudocode | No | The paper contains formal definitions, propositions, proofs, and corollaries, but it does not include any pseudocode or clearly labeled algorithm blocks. |
| Open Source Code | No | The paper does not provide any statement about releasing open-source code or a link to a code repository for the described methodology. |
| Open Datasets | No | This paper is theoretical research and does not involve experiments with datasets. Therefore, no information about training datasets or their public availability is provided. |
| Dataset Splits | No | This paper is theoretical research and does not involve experiments with datasets. Therefore, no information about training/validation/test splits is provided. |
| Hardware Specification | No | This is a theoretical paper and does not involve running experiments that would require specific hardware. Therefore, no hardware specifications are mentioned. |
| Software Dependencies | No | This is a theoretical paper focused on logical frameworks and does not specify any software components with version numbers needed for replication. |
| Experiment Setup | No | This is a theoretical paper and does not involve empirical experiments. Therefore, no details about experimental setup, hyperparameters, or training settings are provided. |