Quantitative Reasoning over Incomplete Abstract Argumentation Frameworks
Authors: Bettina Fazzinga, Sergio Flesca, Filippo Furfaro, Giuseppina Monterosso
IJCAI 2024 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Theoretical | We introduce PERCVER and PERCACC, the problems asking for the percentages of the completions of an incomplete Abstract Argumentation Framework (i AAF) where a set S is an extension and an argument a is accepted, respectively. These problems give insights into the status of S and a more precise than the traditional verification and acceptance tests under the possible and necessary perspectives, that decide if S is an extension and a is accepted in at least one or every completion, respectively. As a first contribution, we study the relationship between the proposed framework and probabilistic AAFs (pr AAFs) under the constellations approach... Then, we investigate the complexity of PERCVER and PERCACC, and identify islands of tractability. |
| Researcher Affiliation | Academia | Bettina Fazzinga1 , Sergio Flesca2 , Filippo Furfaro2 and Giuseppina Monterosso2 1Di CES University of Calabria 2DIMES University of Calabria |
| Pseudocode | No | The paper describes theoretical concepts and proofs but does not include any pseudocode or algorithm blocks. |
| Open Source Code | No | The paper does not provide any information or links regarding the availability of open-source code for the described methodology. |
| Open Datasets | No | The paper is theoretical and does not involve empirical studies or datasets for training. |
| Dataset Splits | No | The paper is theoretical and does not involve empirical studies or dataset splits for validation. |
| Hardware Specification | No | The paper is theoretical and does not describe any hardware used for experiments. |
| Software Dependencies | No | The paper is theoretical and does not list any specific software dependencies with version numbers. |
| Experiment Setup | No | The paper is theoretical and does not describe any experimental setup details, hyperparameters, or training configurations. |