Extension Enforcement in Abstract Argumentation as an Optimization Problem
Authors: Sylvie Coste-Marquis, Sébastien Konieczny, Jean-Guy Mailly, Pierre Marquis
IJCAI 2015 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Intensive experiments show that the method is practical and that it scales up well. |
| Researcher Affiliation | Academia | Sylvie Coste-Marquis S ebastien Konieczny Jean-Guy Mailly Pierre Marquis CRIL, CNRS Universit e d Artois Lens, France {coste,konieczny,mailly,marquis}@cril.fr |
| Pseudocode | No | The paper describes the approach using propositional logic and pseudo-Boolean optimization but does not include any pseudocode or algorithm blocks. |
| Open Source Code | No | The paper mentions wanting to make argumentation reasoning tools available but does not provide concrete access to its own source code, nor does it state that the code is open source. |
| Open Datasets | Yes | We focused on some random AFs [Dvor ak et al., 2011; 2014]. |
| Dataset Splits | No | The paper describes generating random AFs and enforcement requests for experiments, but it does not specify explicit training, validation, or test dataset splits in the traditional sense. |
| Hardware Specification | No | The paper does not provide specific hardware details (e.g., CPU/GPU models, memory) used for running its experiments. |
| Software Dependencies | Yes | We implemented the family of enforcement operators described in this paper, using the well-known tool CPlex [IBM, 2014] as the underlying optimization engine. |
| Experiment Setup | Yes | In our experiments n varies up to 500 arguments. For each n, the graphs are divided into four families, corresponding to four values of p. We used families of AFs from [Dvor ak et al., 2011], where p {0.4, 0.65, 0.9}. We also generated AFs with a probability p = 0.1. |