On Paraconsistent Belief Revision in LP
Authors: Nicolas Schwind, Sébastien Konieczny, Ramón Pino Pérez5879-5887
AAAI 2022 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Theoretical | In this work we discuss how to adapt these postulates when the underlying logic is Priest s LP logic, in order to model a rational change, while being a conservative extension of AGM/KM belief revision. This implies, in particular, to adequately adapt the notion of expansion. We provide a representation theorem and some examples of belief revision operators in this setting. For space reasons the proofs are omitted, but an extended version containing all the proofs is available from http://www.cril.fr/ konieczny/AAAI22-SKP.pdf. |
| Researcher Affiliation | Academia | Nicolas Schwind1, S ebastien Konieczny2, Ram on Pino P erez2 1 National Institute of Advanced Industrial Science and Technology, Tokyo, Japan 2 CRIL CNRS, Universit e d Artois, Lens, France |
| Pseudocode | No | The paper does not contain any pseudocode or clearly labeled algorithm blocks. |
| Open Source Code | No | The paper states that an extended version with proofs is available, but does not provide any link or statement about open-source code for the described methodology. |
| Open Datasets | No | This is a theoretical paper that does not involve empirical experiments with datasets. Therefore, no training data information is provided. |
| Dataset Splits | No | This is a theoretical paper that does not involve empirical experiments with datasets. Therefore, no validation data information is provided. |
| Hardware Specification | No | The paper is theoretical and does not involve computational experiments, so there is no mention of hardware specifications. |
| Software Dependencies | No | The paper is theoretical and does not report on computational experiments that would require specific software dependencies with version numbers. |
| Experiment Setup | No | The paper is theoretical and does not describe any experimental setup or hyperparameters for a computational model. |