Revisiting the Notion of Extension over Incomplete Abstract Argumentation Frameworks
Authors: Bettina Fazzinga, Sergio Flesca, Filippo Furfaro
IJCAI 2020 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Theoretical | The main contribution of this paper is a study of the verification problem for (possible) i-extensions under this revisited definition. In particular, we focus on the complexity characterization of this problem and show that, under several semantics, our revisitation also yields beneficial changes in the complexity. In fact, under the admissible, stable, complete, and grounded semantics, the verification problem, that was shown to be NP-complete in [Baumeister et al., 2018b] for the original i-extension, becomes polynomial-time solvable. In this regard, the proofs of the polynomiality under the complete and the grounded semantics are based on elaborate strategies based on removals/insertions of uncertain arguments and attacks that has no analogous counterpart in the literature. A synopsis of the results on the complexity of the verification problem in [Baumeister et al., 2018b] and of our revisitation is in Table 1. |
| Researcher Affiliation | Academia | Bettina Fazzinga1 , Sergio Flesca2 and Filippo Furfaro2 1 ICAR-CNR, Rende (CS), Italy 2 DIMES, University of Calabria, Rende(CS), Italy fazzinga@icar.cnr.it, {flesca,furfaro}@dimes.unical.it |
| Pseudocode | Yes | Algorithm 1 Deciding IPOSVERσ(S) under σ = co; Algorithm 2 Checking IPOSVERσ(S) under σ = gr |
| Open Source Code | No | No statement about open-sourcing code for the described methodology is found. |
| Open Datasets | No | The paper is theoretical and focuses on complexity characterization; it does not involve training models on datasets. |
| Dataset Splits | No | The paper is theoretical and does not involve empirical validation with dataset splits. |
| Hardware Specification | No | The paper is theoretical and discusses complexity results and algorithms; it does not mention any hardware specifications used for experiments. |
| Software Dependencies | No | The paper is theoretical and discusses complexity results and algorithms; it does not mention specific software dependencies with version numbers for experimental setup. |
| Experiment Setup | No | The paper is theoretical and does not describe an empirical experimental setup with hyperparameters or system-level training settings. |