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].
Redefining ABA+ Semantics via Abstract Set-to-Set Attacks
Authors: Yannis Dimopoulos , Wolfgang Dvorak, Matthias König, Anna Rapberger, Markus Ulbricht, Stefan Woltran
AAAI 2024 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Theoretical | In this work, we tackle both issues: First, we develop a novel abstract argumentation formalism based on set-to-set attacks. We show that our so-called Hyper Argumentation Frameworks (HYPAFs) capture ABA+. Second, we propose relaxed variants of complete and grounded semantics for HYPAFs that yield an extension for all frameworks by design, while still faithfully generalizing the established semantics of Dung-style Argumentation Frameworks. We exploit the newly established correspondence between ABA+ and HYPAFs to obtain variants for grounded and complete ABA+ semantics that are guaranteed to yield an outcome. Finally, we discuss basic properties and provide a complexity analysis. Along the way, we settle the computational complexity of several ABA+ semantics. |
| Researcher Affiliation | Academia | Yannis Dimopoulos1, Wolfgang Dvoˇr ak2, Matthias K onig2, Anna Rapberger3, Markus Ulbricht4, Stefan Woltran2 1University of Cyprus, Department of Computer Science 2TU Wien, Institute of Logic and Computation 3Imperial College London, Department of Computing 4Leipzig University, Department of Computer Science |
| Pseudocode | No | The paper provides formal definitions, lemmas, theorems, and examples, but no specific pseudocode or algorithm blocks are present. |
| Open Source Code | No | The paper does not provide any explicit statements about releasing source code or links to a code repository for the described methodology. |
| Open Datasets | No | The paper is theoretical and does not conduct empirical experiments that would involve training on a dataset. Examples are illustrative, not data-driven. |
| Dataset Splits | No | The paper is theoretical and does not describe empirical validation experiments with dataset splits. |
| Hardware Specification | No | The paper discusses computational complexity but does not specify any hardware (CPU, GPU, etc.) used for running experiments. |
| Software Dependencies | No | The paper mentions other work that uses solvers (e.g., "efficient solvers are available (Lehtonen, Wallner, and J arvisalo 2021a,b)"), but it does not specify any software dependencies with version numbers that are required for replicating the work presented in this paper. |
| Experiment Setup | No | The paper is theoretical and does not describe any empirical experiments, thus no experimental setup details like hyperparameters or system-level training settings are provided. |