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 [1].
Complexity of Credulous and Skeptical Acceptance in Epistemic Argumentation Framework
Authors: Gianvincenzo Alfano, Sergio Greco, Francesco Parisi, Irina Trubitsyna
AAAI 2024 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Theoretical | In this paper we investigate the complexity and expressivity of EAF. To this end, we first introduce the Labeled CAF (LCAF), a variation of CAF where constraints are defined over the alphabet of labeled arguments. Then, we investigate the complexity of credulous and skeptical reasoning and show that: i) EAF is more expressive than i AF (under preferred semantics), ii) although LCAF is a restriction of EAF where modal operators are not allowed, these frameworks have the same complexity, iii) the results for LCAF close a gap in the characterization of the complexity of CAF. Interestingly, even though EAF has the same complexity as LCAF, it allows modeling domain knowledge in a more natural and easy-to-understand way. |
| Researcher Affiliation | Academia | Department of Informatics, Modeling, Electronics and System Engineering, University of Calabria, Italy EMAIL |
| Pseudocode | No | The paper does not contain any structured pseudocode or algorithm blocks. |
| Open Source Code | No | The paper does not contain any explicit statement or link indicating that the source code for the described methodology is publicly available. |
| Open Datasets | No | The paper is theoretical and does not involve empirical studies with datasets for training or evaluation. Examples provided are illustrative, not experimental. |
| Dataset Splits | No | The paper is theoretical and does not involve empirical studies. Therefore, no dataset split information for training, validation, or testing is provided. |
| Hardware Specification | No | The paper is theoretical and does not describe any experimental setup or the specific hardware used to run experiments. |
| Software Dependencies | No | The paper is theoretical and does not describe any specific software dependencies with version numbers used for conducting experiments. It discusses potential algorithmic solutions and solvers in the 'Potential algorithmic solutions' section, but these are not used for the analysis presented in the paper itself. |
| Experiment Setup | No | The paper is theoretical and does not involve an experimental setup with hyperparameters or system-level training settings. |