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; scores should be interpreted as estimates. Full accuracy metrics and methodology are described in [1]
A General Notion of Equivalence for Abstract Argumentation
Authors: Ringo Baumann, Wolfgang Dvořák, Thomas Linsbichler, Stefan Woltran
IJCAI 2017 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Theoretical | We provide exact characterizations and complexity results for deciding our new notion of equivalence. We give exact characterizations of C-relativized equivalence for the five semantics mentioned above; in addition we also show results for conflict-free and naive sets. We provide a complexity analysis for deciding C-relativized equivalence; as corollaries we also obtain insight to the complexity of standard equivalence. Some proofs are only sketched or omitted due to space constraints. Full proofs are available in [Baumann et al., 2017]. |
| Researcher Affiliation | Academia | Ringo Baumann University of Leipzig, Germany EMAIL Wolfgang Dvoˇr ak TU Wien, Austria EMAIL Thomas Linsbichler TU Wien, Austria EMAIL Stefan Woltran TU Wien, Austria EMAIL |
| Pseudocode | No | The paper describes theoretical concepts and mathematical proofs but does not include any structured pseudocode or algorithm blocks. |
| Open Source Code | No | The paper does not provide any concrete access to source code, nor does it state that code for the described methodology is released. |
| Open Datasets | No | This is a theoretical paper focused on characterizations and complexity results, not empirical studies involving datasets for training. |
| Dataset Splits | No | This is a theoretical paper focused on characterizations and complexity results, not empirical studies involving validation sets. |
| Hardware Specification | No | The paper is theoretical and does not describe any experiments that would require specific hardware. Therefore, no hardware specifications are mentioned. |
| Software Dependencies | No | The paper is theoretical and does not describe any experiments or implementations that would require specific software dependencies with version numbers. |
| Experiment Setup | No | The paper is theoretical and does not describe an experimental setup with hyperparameters, training configurations, or system-level settings. |