Notice: This website is continuously updated as new conference proceedings and journal volumes are published. Consequently, the figures presented here may differ from those reported in our publications.
A brief overview of our methodology is provided on the Methods page, along with references to publications documenting our approach and links to the code and data used in each study.
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.
Ranking-based Argumentation Semantics Applied to Logical Argumentation
Authors: Jesse Heyninck, Badran Raddaoui, Christian Straßer
IJCAI 2023 | Venue PDF | LLM Run Details | Input Tokens: 18,676 Total number of tokens sent to the LLM as input for this paper's analysis. | Output Tokens: 3,595 Total number of tokens produced by the LLM (including reasoning/thinking tokens) for this paper's analysis.
| 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. |