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; scores should be interpreted as estimates. Full accuracy metrics and methodology are described in [1]

If Nothing Is Accepted -- Repairing Argumentation Frameworks

Authors: Markus Ulbricht, Ringo Baumann

JAIR 2019 | Venue PDF | LLM Run Details

Reproducibility Variable Result LLM Response
Research Type Theoretical In this article we provide a series of first results for these problems in the context of abstract argumentation theory regarding the two most important reasoning modes, namely credulous as well as sceptical acceptance. Our analysis includes the following problems regarding minimal repairs/diagnoses: existence, verification, computation of one and enumeration of all solutions. The latter problem is tackled with a version of the so-called hitting set duality first introduced by Raymond Reiter in 1987. It turns out that grounded semantics plays an outstanding role not only in terms of complexity, but also as a useful tool to reduce the search space for diagnoses regarding other semantics. Sections 3, 4, and 6 are dedicated to theoretical concepts, duality results, and computational complexity analysis respectively.
Researcher Affiliation Academia Ringo Baumann EMAIL Markus Ulbricht EMAIL Department of Computer Science, Leipzig University, Germany
Pseudocode No The paper describes algorithms and methods in prose, but does not contain any explicitly labeled pseudocode blocks or algorithms.
Open Source Code No The paper does not contain any explicit statements about releasing source code, nor does it provide links to any code repositories.
Open Datasets No The paper uses abstract examples (e.g., Example 1.1, 1.2, 3.1) to illustrate theoretical concepts, but it does not use or provide access to any publicly available datasets for empirical evaluation.
Dataset Splits No The paper is theoretical and does not involve experiments on datasets, therefore, there is no mention of dataset splits.
Hardware Specification No The paper focuses on theoretical analysis and computational complexity; it does not describe any experiments that would require specific hardware, so no hardware specifications are provided.
Software Dependencies No The paper focuses on theoretical concepts and algorithms, without describing a practical implementation. Therefore, no software dependencies with version numbers are mentioned.
Experiment Setup No The paper is theoretical and does not present experimental results from implementations. Consequently, there are no details regarding experimental setup, hyperparameters, or training configurations.