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].
Extension Removal in Abstract Argumentation – An Axiomatic Approach
Authors: Ringo Baumann, Gerhard Brewka2670-2677
AAAI 2019 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Theoretical | Analogously to the well known AGM paradigm we develop an axiomatic approach to the removal problem, i.e. a certain set of axioms will determine suitable manipulations. We prove a series of formal results including conditional and unconditional existence and semantical uniqueness of removal operators as well as various impossibility results and show possible ways out. |
| Researcher Affiliation | Academia | Ringo Baumann, Gerhard Brewka Computer Science Institute, Leipzig University, Germany EMAIL |
| Pseudocode | No | The paper does not contain any pseudocode or algorithm blocks. |
| Open Source Code | No | The paper does not provide any statements or links indicating that open-source code for the methodology is available. |
| Open Datasets | No | This paper is theoretical and does not involve training on datasets. |
| Dataset Splits | No | This paper is theoretical and does not involve data splits for validation. |
| Hardware Specification | No | The paper focuses on theoretical contributions and does not mention any hardware specifications used for experiments. |
| Software Dependencies | No | The paper describes theoretical work and does not specify any software dependencies with version numbers for reproducibility. |
| Experiment Setup | No | The paper describes theoretical work and does not include details about an experimental setup, hyperparameters, or training configurations. |