Evaluation of Arguments from Support Relations: Axioms and Semantics
Authors: Leila Amgoud, Jonathan Ben-Naim
IJCAI 2016 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Theoretical | This paper focuses on argumentation graphs whose nodes are arguments and edges represent supports, thus positive relations, between arguments. Furthermore, each argument has a weight reflecting its basic or intrinsic strength. For the sake of generality, the internal structure of arguments and the origin of arguments and their weights are unspecified. The paper tackles for the first time the question of evaluating the overall strengths of arguments in such graphs, thus of defining semantics for support graphs. It introduces a set of axioms that any semantics should satisfy. Then, it defines three semantics and evaluates them against the axioms. |
| Researcher Affiliation | Academia | IRIT CNRS 118 route de Narbonne F-31062 Toulouse Cedex 9, France amgoud@irit.fr, bennaim@irit.fr |
| Pseudocode | No | The paper defines mathematical functions and theorems, but does not include any structured pseudocode or algorithm blocks labeled as such. |
| Open Source Code | No | The paper does not contain any statement or link providing concrete access to source code for the methodology described. |
| Open Datasets | No | The paper is theoretical and focuses on defining axioms and semantics, not on empirical studies involving datasets, training, or machine learning models. |
| Dataset Splits | No | The paper is theoretical and does not describe experiments with validation datasets or splits. |
| Hardware Specification | No | The paper is theoretical and does not describe computational experiments, thus no hardware specifications are mentioned. |
| Software Dependencies | No | The paper is theoretical and does not mention any specific software dependencies or version numbers. |
| Experiment Setup | No | The paper is theoretical, focusing on definitions and axioms, and therefore does not include details about an experimental setup, hyperparameters, or training configurations. |