Incomplete Argumentation Frameworks: Properties and Complexity

Authors: Gianvincenzo Alfano, Sergio Greco, Francesco Parisi, Irina Trubitsyna5451-5460

AAAI 2022 | Conference PDF | Archive PDF | Plain Text | LLM Run Details

Reproducibility Variable Result LLM Response
Research Type Theoretical In this paper, we first introduce three new satisfaction problems named totality, determinism and functionality, and investigate their computational complexity for both AF and i AF under several semantics. We also investigate the complexity of credulous and skeptical acceptance in i AF under semi-stable semantics a problem left open in the literature. We then show that any i AF can be rewritten into an equivalent one where either only (unattacked) arguments or only attacks are uncertain. Finally, we relate i AF to probabilistic argumentation framework, where uncertainty is quantified.
Researcher Affiliation Academia Department of Informatics, Modeling, Electronics and System Engineering, University of Calabria, Italy {g.alfano, greco, fparisi, i.trubitsyna}@dimes.unical.it
Pseudocode No The paper does not contain any pseudocode or clearly labeled algorithm blocks.
Open Source Code No The paper mentions 'Proofs of our results are available in (Alfano et al. 2021c).' which refers to a technical report for proofs, not open-source code for the described methodology. No other statements or links for code release are provided.
Open Datasets No The paper is theoretical and does not use datasets for empirical evaluation. Therefore, it does not provide information about publicly available datasets.
Dataset Splits No The paper is theoretical and does not involve empirical experiments with datasets that would require training, validation, or test splits. No such information is provided.
Hardware Specification No The paper is theoretical and focuses on computational complexity. It does not describe any empirical experiments that would require specific hardware for execution.
Software Dependencies No The paper is theoretical and focuses on computational complexity. It does not describe any empirical experiments that would require specific software dependencies with version numbers for replication.
Experiment Setup No The paper is theoretical and defines new problems and characterizes their complexity. It does not include an experimental setup with hyperparameters or system-level training settings.