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 Coakley et alK. L. Coakley, T. Snelleman, H. Hoos, and O. E. Gundersen, "The embrace of open science: An analysis of a decade of AI research and 56 800 conference papers," Under Review, 2026..
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. |