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 [1].
Relative Inconsistency Measures for Indefinite Databases with Denial Constraints
Authors: Francesco Parisi, John Grant
IJCAI 2023 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Theoretical | In this paper we investigate relative inconsistency measures for indeļ¬nite databases, which allow for indeļ¬nite or partial information which is formally expressed by means of disjunctive tuples. We introduce a postulate-based deļ¬nition of relative inconsistency measure for indeļ¬nite databases with denial constraints, and investigate the compliance of some relative inconsistency measures with rationality postulates for indeļ¬nite databases as well as for the special case of deļ¬nite databases. Finally, we investigate the complexity of the problem of computing the value of the proposed relative inconsistency measures as well as of the problems of deciding whether the inconsistency value is lower than, greater than, or equal to a given threshold for indeļ¬nite and deļ¬nite databases. |
| Researcher Affiliation | Academia | 1Department of Informatics, Modeling, Electronics and System Engineering University of Calabria, Italy 2Department of Computer Science and UMIACS University of Maryland, College Park, MD, USA EMAIL, EMAIL |
| Pseudocode | No | No, the paper does not contain any pseudocode or algorithm blocks. |
| Open Source Code | No | No, the paper does not provide any concrete access to source code for the described methodology. |
| Open Datasets | No | No, the paper uses illustrative examples (e.g., Table 1: Database Dex) but does not use a dataset for training or evaluation in an empirical study, as the work is theoretical. |
| Dataset Splits | No | No, the paper describes theoretical work and does not perform empirical studies with dataset splits for training, validation, or testing. |
| Hardware Specification | No | No, the paper describes theoretical work and does not mention any specific hardware used for experiments. |
| Software Dependencies | No | No, the paper describes theoretical work and does not mention any specific software dependencies or versions. |
| Experiment Setup | No | No, the paper describes theoretical work and does not include details about an experimental setup, such as hyperparameters or training configurations. |