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 Coakley et alK. L. Coakley, T. Snelleman, H. Hoos, and O. E. Gundersen, "The embrace of open science: An analysis of a decade of AI research and 56 800 conference papers," Under Review, 2026..
Revision by History
Authors: Paolo Liberatore
JAIR 2015 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Theoretical | The technical results provided by this article are: ο¬rst, equivalent formulations for the problem of establishing the existence of an order generating a revision sequence using the natural, lexicographical, restrained and reinforcement revision; second, how an initial order can be built if one exists; third, a complexity characterization. The analysis has shown simple equivalent conditions to the existence of an ordering generating a given series of revisions and results for natural (Boutilier, 1996), restrained (Booth & Meyer, 2006), lexicographic (Spohn, 1988; Nayak, 1994) and reinforcement revisions (Jin & Thielscher, 2007). The conditions allow to construct such an initial ordering if one exists. Using these equivalent conditions, the complexity of establishing the existence of orderings generating a sequence has been established for the considered semantics. |
| Researcher Affiliation | Academia | Paolo Liberatore EMAIL Sapienza University of Rome, DIAG Via Ariosto 25, 00185 Rome, Italy |
| Pseudocode | No | The paper describes logical conditions and proofs, but does not present any structured pseudocode or algorithm blocks. |
| Open Source Code | No | The paper does not provide any statement or link regarding the availability of open-source code for the described methodology. |
| Open Datasets | No | The paper is theoretical and does not describe any experiments using datasets. It uses abstract examples to illustrate concepts, but these are not open datasets used for empirical evaluation. |
| Dataset Splits | No | The paper is theoretical and does not describe experiments with datasets, therefore no information about dataset splits is provided. |
| Hardware Specification | No | The paper is theoretical and does not describe any experiments requiring specific hardware, so no hardware specifications are mentioned. |
| Software Dependencies | No | The paper focuses on theoretical contributions and does not mention any specific software dependencies or version numbers for implementation. |
| Experiment Setup | No | The paper is theoretical and does not describe any empirical experiments, therefore no details about experimental setup or hyperparameters are provided. |