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..
Non-flat ABA Is an Instance of Bipolar Argumentation
Authors: Markus Ulbricht, Nico Potyka, Anna Rapberger, Francesca Toni
AAAI 2024 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Theoretical | We define BAF semantics: novel, albeit similar in spirit to an existing semantics interpreting support as deductive (Boella et al. 2010). We also study basic properties. We show that for complete-based semantics, non-flat ABAFs admit a translation to BAFs w.r.t. our semantics. We propose so-called premise-augmented BAFs and show that they capture all common ABA semantics. We analyse the computational complexity of our BAFs. |
| Researcher Affiliation | Academia | Markus Ulbricht1, Nico Potyka2, Anna Rapberger3, Francesca Toni3 1Sca DS.AI, Department of Computer Science, Leipzig University 2School of Computer Science and Informatics, Cardiff University 3Department of Computing, Imperial College London |
| Pseudocode | No | The paper describes formalisms and definitions but does not contain any pseudocode or algorithm blocks. |
| Open Source Code | No | The paper does not provide any statements about releasing open-source code for the methodology described. |
| Open Datasets | No | The paper is theoretical and does not involve empirical studies with datasets for training or evaluation. Therefore, no information on public datasets for training is provided. |
| Dataset Splits | No | The paper is theoretical and does not involve empirical studies with datasets. Therefore, no information on validation dataset splits is provided. |
| Hardware Specification | No | The paper is theoretical and does not report on experiments requiring specific hardware. Therefore, no hardware specifications are mentioned. |
| Software Dependencies | No | The paper is theoretical and does not report on experiments requiring specific software dependencies with version numbers. |
| Experiment Setup | No | The paper is theoretical and does not describe experimental setups, hyperparameters, or training configurations. |