Rational Inference Relations from Maximal Consistent Subsets Selection
Authors: Sébastien Konieczny, Pierre Marquis, Srdjan Vesic
IJCAI 2019 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Theoretical | We define a general class of monotonic selection relations for comparing maximal consistent subsets and show that it corresponds to the class of rational inference relations. We show that, whenever a monotonic selection relation is used to select the best maximal consistent subsets, the induced inference relation is preferential. Furthermore, it also satisfies rational monotony, i.e., it is a rational inference relation in the sense of [Lehmann and Magidor, 1992]. More than that, we provide a representation theorem showing that the class of rational inference relations of [Lehmann and Magidor, 1992] coincides with the class of skeptical inference relations from selected maximal consistent subsets, where the selection process is achieved using a monotonic selection relation. |
| Researcher Affiliation | Academia | 1CRIL-CNRS, Universit e d Artois, France 2Institut Universitaire de France |
| Pseudocode | No | The paper does not contain structured pseudocode or algorithm blocks. |
| Open Source Code | No | The paper does not provide an explicit statement about releasing source code or a link to a code repository for the described methodology. |
| Open Datasets | No | This is a theoretical paper that does not involve the use of datasets for training or evaluation. |
| Dataset Splits | No | This is a theoretical paper that does not involve the use of validation datasets. |
| Hardware Specification | No | This is a theoretical paper that does not describe hardware used for experiments. |
| Software Dependencies | No | This is a theoretical paper that does not mention specific software dependencies with version numbers. |
| Experiment Setup | No | This is a theoretical paper and does not contain specific experimental setup details or hyperparameters. |