Notice: 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. Full accuracy metrics and methodology are described in [1].
Facets in Argumentation: A Formal Approach to Argument Significance
Authors: Johannes K. Fichte, Nicolas Fröhlich, Markus Hecher, Victor Lagerkvist, Yasir Mahmood, Arne Meier, Jonathan Persson
IJCAI 2025 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Finally, we provide an implementation, and conduct experiments to demonstrate feasibility. Finally, we present experiments that demonstrate the feasibility of our framework. We evaluate our implementation on instances of the ICCMA competition. Implementation and Experiments Implementation We implemented counting of extensions and facets for various semantics into our tool called frame (Facets for Reasoning and Analyzing Meaningful Extensions). ... We ran our experiments on a Ubuntu 11.4.0 Linux 5.15 computer with an eight core Intel i7-14700 CPU 1.5 GHz machine with 64GB of RAM. ... Table 3 presents a survey of our results. |
| Researcher Affiliation | Academia | 1Link oping University, Sweden 2Leibniz Universit at Hannover, Germany 3Univ. Artois, CNRS, France 4Paderborn University, Germany |
| Pseudocode | No | The paper does not contain any sections or figures explicitly labeled as 'Pseudocode' or 'Algorithm', nor does it present structured, code-like procedural steps. |
| Open Source Code | No | We implemented counting of extensions and facets for various semantics into our tool called frame (Facets for Reasoning and Analyzing Meaningful Extensions). We build on the Aspartix system, an ASP-based argumentation system for Dung style abstract argumentation and extensions thereof [Egly et al., 2008]. (The text describes their implementation and tool but does not provide an explicit statement or link for its open-source availability.) |
| Open Datasets | Yes | We evaluate our implementation on instances of the ICCMA competition [Bistarelli et al., 2019]. |
| Dataset Splits | No | The paper evaluates its implementation on instances from the 3rd International Competition on Computational Models of Argumentation (ICCMA 19), which are pre-defined problem instances rather than datasets with explicit train/test/validation splits. |
| Hardware Specification | Yes | We ran our experiments on a Ubuntu 11.4.0 Linux 5.15 computer with an eight core Intel i7-14700 CPU 1.5 GHz machine with 64GB of RAM. |
| Software Dependencies | Yes | We employ Aspartix s ASP encoding and take the ASP solver clingo version 5.7.1 [Gebser et al., 2012] to compute credulous and skeptical consequences and take set differences. |
| Experiment Setup | Yes | We limit the runtime on each instance to 60 seconds for sustainability reasons, as differences become visible already with the limitation, and as a user might not want to wait long when investigating search spaces. |