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; scores should be interpreted as estimates. Full accuracy metrics and methodology are described in [1]
A Semantic Characterization ASP Base Revision
Authors: Laurent Garcia, Claire Lefèvre, Igor Stéphan, Odile Papini, Éric Würbel
JAIR 2019 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Theoretical | The paper presents a semantic characterization of these families of revision operators in terms of answer sets. This semantic characterization allows for equivalently considering the evolution of syntactic logic programs and the evolution of their semantic content. It then studies the logical properties of the proposed operators and gives complexity results. This article is an extension of the conference paper (Garcia, Lef evre, Papini, St ephan, & W urbel, 2017). Besides providing full proofs for all results in the Appendix, we add here also a study of logical properties of the proposed revision operators (Section 6) as well as complexity results (Section 7). |
| Researcher Affiliation | Academia | Laurent Garcia EMAIL Claire Lef evre EMAIL Igor St ephan EMAIL LERIA, University of Angers, France Odile Papini EMAIL Eric W urbel EMAIL Aix Marseille University, University of Toulon CNRS, LIS, Marseille, France |
| Pseudocode | No | The paper describes theoretical concepts, definitions, theorems, and proofs. It does not include any explicitly labeled 'Pseudocode' or 'Algorithm' blocks, nor does it present structured steps formatted like code or an algorithm. The algorithmic discussions in the complexity section refer to general steps for proving complexity classes, not specific pseudocode for implementation. |
| Open Source Code | No | The paper does not contain any statements about releasing code, providing a link to a code repository, or indicating that code is available in supplementary materials for the methodology described in this paper. While it mentions that "some efficient solvers are available (Gebser, Kaufmann, & Schaub, 2012; Leone, Pfeifer, Faber, Eiter, Gottlob, Perri, & Scarcello, 2006)" this refers to third-party tools, not the authors' own implementation. |
| Open Datasets | No | The paper is theoretical and illustrates concepts using small, custom-made examples such as the medical context example in the introduction. It does not utilize any publicly available or open datasets for empirical evaluation, nor does it provide concrete access information (links, DOIs, repositories, or formal citations) for any dataset. |
| Dataset Splits | No | The paper does not use any datasets for experimental evaluation, as it is a theoretical work. Consequently, there is no information provided regarding dataset splits (e.g., training, test, validation percentages or counts). |
| Hardware Specification | No | The paper is theoretical and focuses on logical properties and complexity results. It does not describe any experiments that would require specific hardware. Therefore, no details about CPU models, GPU types, memory, or computing environments are provided. |
| Software Dependencies | No | The paper is theoretical and does not describe an implementation of its proposed operators. While it mentions 'efficient solvers' (Gebser, Kaufmann, & Schaub, 2012; Leone, Pfeifer, Faber, Eiter, Gottlob, Perri, & Scarcello, 2006) in the context of Answer Set Programming, these are general references to existing tools, not specific software dependencies with version numbers used for the authors' work or experiments. |
| Experiment Setup | No | The paper is theoretical and presents definitions, logical properties, and complexity analysis. It does not describe any empirical experiments. Therefore, no experimental setup details, such as hyperparameter values, training configurations, or system-level settings, are present in the main text. |