Iterated Belief Base Revision: A Dynamic Epistemic Logic Approach

Authors: Marlo Souza, Álvaro Moreira, Renata Vieira3076-3083

AAAI 2019 | Conference PDF | Archive PDF | Plain Text | 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.