Comparing Options with Argument Schemes Powered by Cancellation
Authors: Khaled Belahcene, Christophe Labreuche, Nicolas Maudet, Vincent Mousseau, Wassila Ouerdane
IJCAI 2019 | Conference PDF | Archive PDF | Plain Text | 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. |