On the Graded Acceptability of Arguments
Authors: Davide Grossi, Sanjay Modgil
IJCAI 2015 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Theoretical | The paper develops a formal theory of the degree of justification of arguments, which relies solely on the structure of an argumentation framework. The theory is based on a generalisation of Dung s notion of acceptability, making it sensitive to the numbers of attacks and counter-attacks on arguments. Graded generalisations of argumentation semantics are then obtained and studied. The theory is applied by showing how it can arbitrate between competing preferred extensions and how it captures a specific form of accrual in instantiated argumentation.Proofs do not pose particular challenges and make use of standard techniques (e.g., fixpoint theory). They are omitted for space reasons. |
| Researcher Affiliation | Academia | Davide Grossi Department of Computer Science University of Liverpool d.grossi@liverpool.ac.uk Sanjay Modgil Department of Informatics King s College London sanjay.modgil@kcl.ac.uk |
| Pseudocode | No | The paper defines formal functions and theorems but does not include any explicitly labeled pseudocode or algorithm blocks. |
| Open Source Code | No | The paper does not provide any statement or link indicating that the source code for the described methodology is publicly available. |
| Open Datasets | No | This paper is theoretical and primarily uses illustrative examples (e.g., Figure 1) rather than empirical evaluation on datasets, so there is no mention of a publicly available dataset for training. |
| Dataset Splits | No | The paper is theoretical and does not describe experiments involving dataset splits for training, validation, or testing. |
| Hardware Specification | No | The paper is theoretical and does not describe any experiments that would require hardware specifications, so none are provided. |
| Software Dependencies | No | The paper is theoretical and does not describe any computational experiments that would require specific software dependencies or version numbers. |
| Experiment Setup | No | The paper is theoretical and focuses on formal definitions and properties rather than practical experimental setups, thus no specific hyperparameter values or training configurations are provided. |