Non-flat ABA Is an Instance of Bipolar Argumentation
Authors: Markus Ulbricht, Nico Potyka, Anna Rapberger, Francesca Toni
AAAI 2024 | Conference PDF | Archive PDF | Plain Text | 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. |