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.
A Study of Argumentative Characterisations of Preferred Subtheories
Authors: Marcello D'Agostino, Sanjay Modgil
IJCAI 2018 | Venue PDF | LLM Run Details | Input Tokens: 17,266 Total number of tokens sent to the LLM as input for this paper's analysis. | Output Tokens: 3,882 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 | This paper is the first to establish these characterisations for the preferred semantics, by showing that the preferred and stable extensions of (dialectical) AFs instantiated by default theories coincide.These results imply that argument game proof theories and algorithms for deciding membership of arguments in admissible extensions, suffice for realising the practical use of argumentation in deciding credulous Preferred Subtheories inference by single agents, and in distributed reasoning amongst multiple agents. |
| Researcher Affiliation | Academia | 1 Department of Philosophy, University of Milan 2 Department of Informatics, King s College London EMAIL, EMAIL |
| Pseudocode | No | The paper does not contain structured pseudocode or algorithm blocks. |
| Open Source Code | No | The paper does not provide concrete access to source code. |
| Open Datasets | No | The paper is theoretical and does not use or refer to any publicly available datasets for experimental evaluation. |
| Dataset Splits | No | The paper is theoretical and does not describe dataset splits for training, validation, or testing. |
| Hardware Specification | No | The paper is theoretical and does not describe hardware used for experiments. |
| Software Dependencies | No | The paper is theoretical and does not mention specific software dependencies for any experimental setup. |
| Experiment Setup | No | The paper is theoretical and does not describe an experimental setup with hyperparameters or training configurations. |