Abstract Argumentation Framework with Conditional Preferences
Authors: Gianvincenzo Alfano, Sergio Greco, Francesco Parisi, Irina Trubitsyna
AAAI 2023 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Theoretical | After introducing CPAF, we study the complexity of the verification problem (deciding whether a set of arguments is a best extension) as well as of the credulous and skeptical acceptance problems (deciding whether a given argument belongs to any or all best extensions, respectively) under multiple-status semantics (that is, complete, preferred, stable, and semi-stable semantics) for the abovementioned preference criteria. The complexity of the verification problem does not depend on the flat or closed interpretation. Moreover, the complexity bounds for all the three problems for CPAF coincide with those known for PAF, though more general preferences can be expressed in CPAF. The results of this section show that the complexity of the three considered problems for CPAF generally increases of one level in the polynomial hierarchy w.r.t that of AF. |
| 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 provide any concrete access to source code (e.g., specific repository link, explicit code release statement) for the methodology described. |
| Open Datasets | No | The paper is theoretical and does not involve empirical training on datasets. Therefore, no information about public datasets for training is provided. |
| Dataset Splits | No | The paper is theoretical and does not involve empirical experiments with data splits. Therefore, no specific dataset split information (training, validation, test) is provided. |
| Hardware Specification | No | The paper is theoretical and does not mention any specific hardware used for running experiments. |
| Software Dependencies | No | The paper is theoretical and does not mention any specific ancillary software details with version numbers (e.g., libraries, solvers, programming languages beyond general concepts). |
| Experiment Setup | No | The paper is theoretical and does not describe an experimental setup with hyperparameters or training configurations. |