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..
Parameterized Complexity of Logic-Based Argumentation in Schaefer’s Framework
Authors: Yasir Mahmood, Arne Meier, Johannes Schmidt6426-6434
AAAI 2021 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Theoretical | Analyzing the reason for this intractability, we perform a two-dimensional classification: first, we consider all possible propositional fragments of the problem within Schaefer s framework, and then study different parameterizations for each of the fragment. We identify a list of reasonable structural parameters (size of the claim, support, knowledgebase) that are connected to the aforementioned decision problems. Eventually, we thoroughly draw a fine border of parameterized intractability for each of the problems showing where the problems are fixed-parameter tractable and when this exactly stops. |
| Researcher Affiliation | Academia | 1Leibniz Universität Hannover, Institut für Theoretische Informatik, Germany 2Jönköping University, Department of Computer Science and Informatics, School of Engineering, Sweden |
| Pseudocode | No | The paper presents theoretical definitions, lemmas, theorems, and proofs related to complexity theory, but it does not include any pseudocode or algorithm blocks. |
| Open Source Code | No | The paper does not include any explicit statement or link indicating that the authors have made their source code publicly available for the work described. |
| Open Datasets | No | The paper is theoretical and focuses on complexity classification; it does not use or reference any datasets for training or evaluation. |
| Dataset Splits | No | The paper is theoretical and does not involve empirical experiments or datasets, therefore it does not discuss training, validation, or test splits. |
| Hardware Specification | No | The paper is theoretical and does not describe any experiments that would require specific hardware. Consequently, no hardware specifications are mentioned. |
| Software Dependencies | No | The paper is theoretical and does not specify any software dependencies with version numbers for reproducing experiments. |
| Experiment Setup | No | The paper is theoretical and does not describe any empirical experiments or their setup, thus there are no hyperparameters or training configurations mentioned. |