Featured Argumentation Framework: Semantics and Complexity

Authors: Gianvincenzo Alfano, Sergio Greco, Francesco Parisi, Irina Trubitsyna

IJCAI 2025 | Venue PDF | Archive PDF | Plain Text | LLM Run Details

Reproducibility Variable Result LLM Response
Research Type Theoretical We investigate the computational complexity of verification and acceptance problems under several semantics and show that incomplete AF (i AF) frameworks, including correlated i AF and constrained i AF, are special cases of EFAF. Table 1: Complexity of the verification problems for AF, i AF, ci AF, and EFAF under semantics σ {gr, co, pr, st}. Table 2: Complexity of possible and necessary credulous/skeptical acceptance under semantics σ {gr, co, st} for AF, FAF, i AF, ci AF, and EFAF.
Researcher Affiliation Academia Gianvincenzo Alfano , Sergio Greco , Francesco Parisi and Irina Trubitsyna DIMES Department, University of Calabria, Rende, Italy EMAIL
Pseudocode No The paper only describes new formalisms (FAF, EFAF) and analyzes their computational complexity. It does not include any structured pseudocode or algorithm blocks.
Open Source Code No The paper does not contain any explicit statements about providing open-source code for the methodology described, nor does it provide any links to code repositories.
Open Datasets No The paper uses conceptual examples like 'air pollution experts' and 'policies' to illustrate the argumentation framework but does not conduct empirical evaluations using specific datasets, and therefore provides no information about open datasets.
Dataset Splits No The paper does not conduct empirical experiments using datasets, thus there is no information regarding dataset splits.
Hardware Specification No The paper is theoretical and focuses on semantics and complexity, without reporting on experimental results. Therefore, it does not specify any hardware used for experiments.
Software Dependencies No The paper describes theoretical frameworks and their complexity, and does not mention any specific software dependencies with version numbers for experimental reproducibility.
Experiment Setup No The paper introduces theoretical frameworks and analyzes their complexity; it does not describe any experimental setup details such as hyperparameters or system-level training settings.