Notice: The reproducibility variables underlying each score are classified using an automated LLM-based pipeline, validated against a manually labeled dataset. LLM-based classification introduces uncertainty and potential bias; scores should be interpreted as estimates. Full accuracy metrics and methodology are described in [1].
On Preferences and Priority Rules in Abstract Argumentation
Authors: Gianvincenzo Alfano, Sergio Greco, Francesco Parisi, Irina Trubitsyna
IJCAI 2022 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Theoretical | In this paper we first investigate the complexity of the verification as well as credulous and skeptical acceptance problems in Preferencebased AF (PAF) that extends AF with preferences over arguments. Next, after introducing new semantics for AF where extensions are selected using cardinality (instead of set inclusion) criteria and investigating their complexity, we introduce a framework called AF with Priority rules (AFP) that extends AF with sequences of priority rules. AFP generalizes AF with classical set-inclusion and cardinality based semantics, suggesting that argumentation semantics can be viewed as ways to express priorities among extensions. Finally, we extend AFP by proposing AF with Priority rules and Preferences (AFP2), where also preferences over arguments can be used to define priority rules, and study the complexity of the above-mentioned problems. |
| Researcher Affiliation | Academia | Gianvincenzo Alfano , Sergio Greco , Francesco Parisi and Irina Trubitsyna DIMES Department, University of Calabria, Rende, Italy EMAIL |
| Pseudocode | No | The paper does not contain any pseudocode or clearly labeled algorithm blocks. |
| Open Source Code | No | As for implementations of our framework, given the connection between AF semantics and LP models [Caminada et al., 2015; Alfano et al., 2020b], ASP implementations such as DLV and potassco that support cardinality-based semantics can be used to define encodings for AFP semantics by extending those for AF [Dvor ak et al., 2020]. |
| Open Datasets | No | The paper is theoretical and does not use or reference publicly available datasets with access information (link, DOI, repository, or formal citation). |
| Dataset Splits | No | The paper is theoretical and does not specify dataset splits (training, validation, test). |
| Hardware Specification | No | The paper is theoretical and does not describe any specific hardware used for experiments. |
| Software Dependencies | No | The paper mentions 'ASP implementations such as DLV and potassco' and 'YALLA' but does not provide specific version numbers for any software dependencies. |
| Experiment Setup | No | The paper is theoretical and does not describe any specific experimental setup details, hyperparameters, or training configurations. |