General Epistemic Abstract Argumentation Framework: Semantics and Complexity
Authors: Gianvincenzo Alfano, Sergio Greco, Francesco Parisi, Irina Trubitsyna
IJCAI 2024 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Theoretical | In this paper, we provide an intuitive semantics for (general) EAAF that naturally extends that for AAF as well as that for acyclic EAAF. After providing some fundamental properties and giving an algorithm that enables the computation of EAAF semantics, by relying on state-of-the-art AAF-solvers, we investigate the complexity of canonical argumentation problems. Our complexity results are summarized in Table 2 (in Section 5). |
| Researcher Affiliation | Academia | Gianvincenzo Alfano , Sergio Greco , Francesco Parisi and Irina Trubitsyna DIMES Department, University of Calabria, Rende, Italy {g.alfano, greco, fparisi, i.trubitsyna}@dimes.unical.it |
| Pseudocode | Yes | Algorithm 1 Solve EAAF |
| Open Source Code | No | The paper does not provide explicit statements or links to open-source code for the described methodology. It mentions reliance on 'state-of-the-art AAFsolvers' but not the release of their own implementation. |
| Open Datasets | No | The paper is theoretical, focusing on semantics and complexity, and does not involve the use of datasets for training or evaluation. Therefore, no information on publicly available datasets is provided. |
| Dataset Splits | No | The paper is theoretical and does not involve data splits for training, validation, or testing. Therefore, no information on dataset splits is provided. |
| Hardware Specification | No | The paper is theoretical and does not describe any computational experiments that would require specific hardware. No hardware specifications are mentioned. |
| Software Dependencies | No | The paper mentions 'state-of-the-art AAFsolvers' as external tools but does not specify software dependencies with version numbers for their own work or implementation of Algorithm 1. |
| Experiment Setup | No | The paper is theoretical and does not describe empirical experiments with specific hyperparameters or training settings. No experimental setup details are provided. |