Complexity of Credulous and Skeptical Acceptance in Epistemic Argumentation Framework

Authors: Gianvincenzo Alfano, Sergio Greco, Francesco Parisi, Irina Trubitsyna

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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 {g.alfano, greco, fparisi, i.trubitsyna}@dimes.unical.it
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.