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].
The Complexity Landscape of Claim-Augmented Argumentation Frameworks
Authors: Wolfgang Dvořák, Alexander Greßler, Anna Rapberger, Stefan Woltran6296-6303
AAAI 2021 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Theoretical | To this end, we provide a comprehensive complexity analysis of the four main reasoning problems with respect to claim-level variants of preferred, naive, stable, semi-stable and stage semantics and complete the complexity results of inherited semantics by providing corresponding results for semi-stable and stage semantics. Our main contributions are as follows: We settle the computational complexity of all the claim-level semantics... |
| Researcher Affiliation | Academia | Wolfgang Dvoˇr ak, Alexander Greßler, Anna Rapberger, Stefan Woltran TU Wien, Institute of Logic and Computation, Austria EMAIL |
| Pseudocode | No | The paper does not contain any pseudocode or algorithm blocks. It focuses on theoretical proofs and complexity analysis. |
| Open Source Code | No | The paper does not provide any links to open-source code or state that code for their methodology is available. |
| Open Datasets | No | This paper is theoretical and does not involve empirical evaluation with datasets. |
| Dataset Splits | No | This paper is theoretical and does not involve empirical evaluation with datasets or data splitting. |
| Hardware Specification | No | This is a theoretical paper focusing on complexity analysis and does not describe any experiments that would require hardware specifications. |
| Software Dependencies | No | This paper is theoretical and does not describe any software implementations or their dependencies. |
| Experiment Setup | No | This paper is theoretical and does not describe any experimental setup, hyperparameters, or training configurations. |