Measuring the Intensity of Attacks in Argumentation Graphs with Shapley Value
Authors: Leila Amgoud, Jonathan Ben-Naim, Srdjan Vesic
IJCAI 2017 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Theoretical | This paper introduces the novel concept of contribution measure for evaluating those contributions. It starts by defining a set of axioms that a reasonable measure would satisfy, then shows that the Shapley value is the unique measure that satisfies them. Finally, it investigates the properties of the latter under some existing semantics. |
| Researcher Affiliation | Academia | Leila Amgoud1, Jonathan Ben-Naim1, Srdjan Vesic2 1 IRIT, CNRS Universit e de Toulouse, France 2 CRIL, CNRS Universit e d Artois, France |
| Pseudocode | No | The paper does not contain any pseudocode or clearly labeled algorithm blocks. |
| Open Source Code | No | The paper does not provide any concrete access information (e.g., repository link, explicit statement of code release) for open-source code related to the methodology described. |
| Open Datasets | No | The paper is theoretical and does not use or reference any specific publicly available datasets for training or evaluation. The examples used (e.g., A1, A2, A3, A4) are conceptual illustrations, not actual datasets. |
| Dataset Splits | No | The paper is theoretical and does not conduct experiments involving dataset splits for training, validation, or testing. |
| Hardware Specification | No | The paper does not provide any specific hardware details (e.g., GPU/CPU models, processor types, memory amounts) used for running experiments, as it is a theoretical paper. |
| Software Dependencies | No | The paper does not provide any specific ancillary software details with version numbers (e.g., library or solver names with version numbers) needed to replicate experiments. |
| Experiment Setup | No | The paper is theoretical and does not include details about an experimental setup, such as hyperparameters or system-level training settings. |