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].
Robustness in Single-Audience Value-based Abstract Argumentation: Complexity Results
Authors: Bettina Fazzinga, Sergio Flesca, Filippo Furfaro
IJCAI 2025 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Theoretical | Herein, we introduce a new notion of robustness for measuring the sensitivity of the outcome of the reasoning to the extent of changes in the audience profile. In particular, for a set of arguments S or a single argument a, we define the robustness degree of the status of S or a as the maximum number k of deletions/insertions of preferences from/into the audience profile that are tolerable, in the sense that S remains an extension (or a non-extension) or a accepted (or unaccepted) after performing at most k deletions/insertions. We introduce the decision problems related to the computation of the robustness degree and focus on thoroughly investigating their computational complexity. |
| Researcher Affiliation | Academia | Bettina Fazzinga1 , Sergio Flesca2 and Filippo Furfaro2 1Di CES University of Calabria 2DIMES University of Calabria EMAIL |
| Pseudocode | No | The paper presents theoretical concepts, definitions, lemmas, and theorems. It does not include any explicitly labeled pseudocode blocks or algorithms. |
| Open Source Code | No | The paper is theoretical, focusing on computational complexity results for robustness in argumentation frameworks. There is no mention of any code release or a link to a code repository. |
| Open Datasets | No | The paper is theoretical and does not conduct empirical studies that would require the use of open datasets. It uses examples to illustrate concepts, but these are not datasets in the context of experimental evaluation. |
| Dataset Splits | No | The paper is theoretical and does not involve experimental evaluation using datasets, therefore, there is no information regarding dataset splits. |
| Hardware Specification | No | The paper is theoretical and focuses on computational complexity results. It does not describe any experiments that would require specific hardware, thus no hardware specifications are provided. |
| Software Dependencies | No | The paper is theoretical and focuses on computational complexity results. It does not describe any experimental implementation or specific software requirements with version numbers. |
| Experiment Setup | No | The paper is theoretical, introducing a new notion of robustness and investigating its computational complexity. It does not describe any empirical experiments or their setup, thus no hyperparameters or training settings are mentioned. |