Abstract Argumentation Frameworks with Marginal Probabilities
Authors: Bettina Fazzinga, Sergio Flesca, Filippo Furfaro
IJCAI 2022 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Theoretical | We focus on the problems of computing the max and min probabilities of extensions over m AAFs under Dung s semantics, characterize their complexity, and provide closed formulas for polynomial cases. We start with the problem MAXP-VER and show that it can be solved in polynomial time under the conflict-free, admissible, and stable semantics, that it is complete for NP under the complete and grounded semantics, and for Σp 2-complete under the preferred semantics. |
| Researcher Affiliation | Academia | Bettina Fazzinga1,2 and Sergio Flesca3 and Filippo Furfaro3 1DICES University of Calabria, Italy 2ICAR CNR, Italy 3DIMES University of Calabria, Italy {bettina.fazzinga, sergio.flesca, filippo.furfaro}@unical.it |
| Pseudocode | No | The paper contains mathematical definitions, theorems, and proofs but does not include any pseudocode or clearly labeled algorithm blocks. |
| Open Source Code | No | The paper does not provide any statement or link indicating that the source code for the described methodology is publicly available. |
| Open Datasets | No | This is a theoretical paper and does not involve the use of datasets for training, validation, or testing. |
| Dataset Splits | No | This is a theoretical paper and does not involve the use of datasets for training, validation, or testing. |
| Hardware Specification | No | The paper does not mention any specific hardware used for experiments, as it is a theoretical work. |
| Software Dependencies | No | The paper does not mention any specific software dependencies with version numbers, as it is a theoretical work focusing on complexity analysis. |
| Experiment Setup | No | The paper focuses on theoretical analysis and complexity characterization and does not include details on experimental setup, hyperparameters, or training configurations. |