Ranking-based Argumentation Semantics Applied to Logical Argumentation
Authors: Jesse Heyninck, Badran Raddaoui, Christian Straßer
IJCAI 2023 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Theoretical | In this paper, we make a systematic investigation into the behaviour of ranking-based semantics applied to existing formalisms for structured argumentation. We show that a wide class of ranking-based semantics gives rise to so-called culpability measures, and are relatively robust to specific choices in argument construction methods. |
| Researcher Affiliation | Academia | Jesse Heyninck1, Badran Raddaoui2,3, Christian Straßer3 1 Open Universiteit, the Netherlands 2 SAMOVAR, T el ecom Sud Paris, Institut Polytechnique de Paris, France 3 Institute for Philosophy II, Ruhr-Universit at Bochum, Germany |
| Pseudocode | No | The paper does not contain any pseudocode or clearly labeled algorithm blocks. |
| Open Source Code | No | The paper does not provide any explicit statements or links indicating that open-source code for the described methodology is available. |
| Open Datasets | No | The paper is theoretical and uses illustrative examples (e.g., Example 1, Example 2) rather than publicly available datasets for training or evaluation. No dataset access information is provided. |
| Dataset Splits | No | The paper is theoretical and does not describe empirical experiments that would require training, validation, or test dataset splits. |
| Hardware Specification | No | The paper does not mention any specific hardware used for experiments, as it is a theoretical work. |
| Software Dependencies | No | The paper mentions the 'Tweetylibrary [Thimm, 2014]' as an example for verification, but it does not list any specific software dependencies with version numbers for reproducing the paper's theoretical framework or its illustrative examples. |
| Experiment Setup | No | The paper is theoretical and does not describe empirical experiments, thus no experimental setup details like hyperparameters or training settings are provided. |