Inconsistency Measurement for Paraconsistent Inference
Authors: Yakoub Salhi
IJCAI 2021 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Theoretical | One of the main aims of the methods developed for reasoning under inconsistency, in particular paraconsistent inference, is to derive informative conclusions from inconsistent bases. In this paper, we introduce an approach based on inconsistency measurement for defining non-monotonic paraconsistent consequence relations. We first exhibit interesting properties of our consequence relations. We then study situations where they bring about consequences that are always jointly consistent. In particular, we introduce a property of inconsistency measures that guarantees the consistency of the set of all entailed formulas. We also show that this property leads to several interesting properties of our IM-based consequence relations. Finally, we discuss relationships between our framework and well-known consequence relations that are based on maximal consistent subsets. In this setting, we establish direct connections between the latter and properties of inconsistency measures. |
| Researcher Affiliation | Academia | Yakoub Salhi CRIL, Universit e d Artois & CNRS, France salhi@cril.fr |
| Pseudocode | No | The paper does not contain pseudocode or clearly labeled algorithm blocks; it presents definitions, propositions, and theorems. |
| Open Source Code | No | The paper does not provide any concrete access to source code for the methodology described. |
| Open Datasets | No | This is a theoretical paper and does not involve the use of datasets for training. |
| Dataset Splits | No | This is a theoretical paper and does not involve the use of datasets or their splits for validation. |
| Hardware Specification | No | This is a theoretical paper and does not describe any experimental hardware specifications. |
| Software Dependencies | No | This is a theoretical paper and does not list any software dependencies with specific version numbers. |
| Experiment Setup | No | This is a theoretical paper and does not describe any experimental setup details such as hyperparameters or training configurations. |