Complexity of Abstract Argumentation under a Claim-Centric View
Authors: Wolfgang Dvořák, Stefan Woltran2801-2808
AAAI 2019 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Theoretical | Complexity analysis of abstract argumentation so far has neglected this final step and is concerned with argument names instead of their contents, i.e. their claims. As we outline in this paper, this is not only a slight deviation but can lead to different complexity results. We, therefore, give a comprehensive complexity analysis of abstract argumentation under a claim-centric view and analyse the four main decision problems under seven popular semantics. In addition, we also address the complexity of common sub-classes and introduce novel parameterisations which exploit the nature of claims explicitly along with fixed-parameter tractability results. |
| Researcher Affiliation | Academia | Wolfgang Dvoˇr ak, Stefan Woltran TU Wien Vienna, Austria {dvorak,woltran}@dbai.tuwien.ac.at |
| Pseudocode | No | The paper presents theoretical definitions, propositions, and theorems, but it does not include any pseudocode or algorithm blocks. |
| Open Source Code | No | The paper is theoretical and does not mention the release of any source code. |
| Open Datasets | No | The paper is theoretical and does not involve experimental evaluation on datasets, thus no dataset access information is provided. |
| Dataset Splits | No | The paper is theoretical and does not involve experimental evaluation on datasets, thus no dataset split information for training, validation, or testing is provided. |
| Hardware Specification | No | The paper is theoretical and does not describe any experimental setup that would require specific hardware specifications. |
| Software Dependencies | No | The paper is theoretical and does not describe an experimental setup requiring specific software dependencies with version numbers. |
| Experiment Setup | No | The paper is theoretical and focuses on complexity analysis and proofs, rather than describing an experimental setup with hyperparameters or training configurations. |