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..
The Effect of Preferences in Abstract Argumentation under a Claim-Centric View
Authors: Michael Bernreiter, Wolfgang Dvorak, Anna Rapberger, Stefan Woltran
AAAI 2023 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Theoretical | In this paper, we study the effect of preferences in abstract argumentation under a claim-centric perspective. Our main contributions are as follows: For each of the four reductions, we characterize the possible structure of CAFs that are obtained by applying the reduction to a well-formed CAF and a preference relation. We investigate the relationship between these classes. We study I-maximality of stable, preferred, semi-stable, stage, and naive semantics of the novel CAF classes. Finally, we investigate the complexity of reasoning for CAFs with preferences with respect to conflict-free, admissible, complete, and all of the aforementioned semantics. |
| Researcher Affiliation | Academia | Michael Bernreiter, Wolfgang Dvoˇr ak, Anna Rapberger, Stefan Woltran Institute of Logic and Computation, TU Wien, Austria EMAIL |
| Pseudocode | No | The paper does not contain structured pseudocode or algorithm blocks. |
| Open Source Code | No | The paper does not provide concrete access to source code. It is a theoretical paper and does not mention any code release. |
| Open Datasets | No | The paper does not describe experiments using datasets, thus no information on public dataset availability is provided. |
| Dataset Splits | No | The paper does not describe experiments using datasets, thus no information on dataset splits for validation is provided. |
| Hardware Specification | No | The paper is theoretical and does not describe any experimental setup or hardware used. |
| Software Dependencies | No | The paper is theoretical and does not describe any experimental setup or specific software dependencies with version numbers. |
| Experiment Setup | No | The paper is theoretical and does not describe any experimental setup details such as hyperparameters or training configurations. |