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..
Control Argumentation Frameworks
Authors: Yannis Dimopoulos, Jean-Guy Mailly, Pavlos Moraitis
AAAI 2018 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Theoretical | This work proposes Control Argumentation Frameworks (CAFs), a new approach that generalizes existing techniques, namely normal extension enforcement, by accommodating the possibility of uncertainty in dynamic scenarios. A QBF encoding of reasoning with CAFs provides a computational mechanism for determining whether and how this goal can be reached. We also provide some results concerning soundness and completeness of the proposed encoding as well as complexity issues. |
| Researcher Affiliation | Academia | Yannis Dimopoulos Department of Computer Science University of Cyprus EMAIL Jean-Guy Mailly LIPADE Paris Descartes University, France EMAIL Pavlos Moraitis LIPADE Paris Descartes University, France EMAIL |
| Pseudocode | Yes | Algorithm 1 CAFControl Require: CAF = F, C, U , T AF , x {sk, cr} QBFsk(CAF, T) is the formula defined by (1) QBFcr(CAF, T) is the formula defined by (2) if QBFTruth(QBFx(CAF, T)) then Aconf = {xi AC | onxi is assigned 1 in QBFModel(QBFx(CAF, T))} return Aconf else return end if |
| Open Source Code | No | The paper does not contain any statement about releasing open-source code for the methodology, nor does it provide a link to a code repository. |
| Open Datasets | No | The paper is theoretical and focuses on developing a new argumentation framework and its logical encoding. It does not mention or use any specific datasets for training, validation, or testing. |
| Dataset Splits | No | The paper is theoretical and does not describe experiments involving dataset splits for training, validation, or testing. Therefore, no specific dataset split information is provided. |
| Hardware Specification | No | The paper is theoretical and does not describe empirical experiments that would require specific hardware. Therefore, no hardware specifications are mentioned. |
| Software Dependencies | No | The paper mentions using "QBFs" and refers to "QBF solvers" (Pulina 2016), but it does not specify any particular QBF solver by name with a version number, or other software dependencies with versions required for reproducibility. |
| Experiment Setup | No | The paper is theoretical and focuses on formal definitions, logical encodings, and complexity analysis. It does not describe any empirical experimental setup, hyperparameters, or training configurations. |