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..
Comparing Options with Argument Schemes Powered by Cancellation
Authors: Khaled Belahcene, Christophe Labreuche, Nicolas Maudet, Vincent Mousseau, Wassila Ouerdane
IJCAI 2019 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Theoretical | We formalize and streamline this procedure with argument schemes. As a result, any conclusion drawn by means of this approach comes along with a justification. It turns out that the statements which can be inferred through this process form a proper preference relation. More precisely, it corresponds to a necessary preference relation under the assumption of additive utilities. We show the inference task can be performed in polynomial time in this setting, but that finding a minimal length explanation is NP-complete. |
| Researcher Affiliation | Collaboration | Khaled Belahcene1 , Christophe Labreuche2 , Nicolas Maudet3 , Vincent Mousseau4 and Wassila Ouerdane4 1 Nutriomics, Sorbonne Universit e, INSERM, France 2 Thales Research and Technology, Palaiseau, France 3Sorbonne Universit e, CNRS, LIP6, F-75005 Paris, France 4MICS, Centrale Sup elec, Universit e Paris-Saclay, Gif-sur-Yvette, France |
| Pseudocode | No | The paper contains formal definitions, theorems, and proofs, but no clearly labeled pseudocode or algorithm blocks. |
| Open Source Code | No | The paper does not contain any statements or links indicating the release of open-source code for the described methodology. |
| Open Datasets | No | The paper is theoretical and does not describe experiments involving datasets, training, or validation splits. |
| Dataset Splits | No | The paper is theoretical and does not describe experiments involving datasets, training, or validation splits. |
| Hardware Specification | No | The paper is theoretical and does not report on computational experiments, thus no hardware specifications are provided. |
| Software Dependencies | No | The paper is theoretical and does not report on computational experiments, thus no software dependencies with version numbers are provided. |
| Experiment Setup | No | The paper is theoretical and does not describe an experimental setup with hyperparameters or system-level training settings. |