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].
On Independence and SCC-Recursiveness in Assumption-Based Argumentation
Authors: Lydia Blümel, Anna Rapberger, Matthias Thimm, Francesca Toni
IJCAI 2025 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Theoretical | We introduce a notion of conditional independence in (flat) assumption-based argumentation (ABA), where independence between (sets of) assumptions amounts to the presence of information about one set of assumptions not impacting the acceptability of another. We study general properties, computational complexity, and the relation to independence in abstract argumentation. In light of the high computational complexity of deciding independence, we introduce sound methods for checking independence in polynomial time via two different routes: the first utilizes the strongly connected components (SCCs) of the instantiated abstract argumentation framework; the second exploits the structure of the ABA framework directly. Along the way, we introduce the notion of SCC-recursiveness for ABA. |
| Researcher Affiliation | Academia | 1Artificial Intelligence Group, University of Hagen 2Imperial College London EMAIL, EMAIL |
| Pseudocode | No | The paper describes methods and concepts through definitions, propositions, theorems, and examples, but it does not contain any clearly labeled pseudocode or algorithm blocks with structured steps. |
| Open Source Code | No | Due to space restrictions, we focus on complete-based semantics; analogous results for admissible semantics, alongside with proofs and further discussions are included in an extended version found online (DOI 10.5281/zenodo.15470789, https://zenodo.org/records/15470789). |
| Open Datasets | No | The paper primarily uses illustrative examples (e.g., Example 1.1, 3.2, 4.2) to explain theoretical concepts, rather than conducting experiments on external publicly available datasets. |
| Dataset Splits | No | The paper does not use any external datasets for experiments, and therefore does not discuss dataset splits. |
| Hardware Specification | No | The paper focuses on theoretical contributions and does not report on experimental results that would require specific hardware. Therefore, no hardware specifications are mentioned. |
| Software Dependencies | No | The paper presents theoretical concepts and methods, and does not describe any implementation details or report on experimental setups that would require specific software dependencies or version numbers. |
| Experiment Setup | No | The paper focuses on theoretical developments and does not include an experimental section detailing hyperparameters, training configurations, or system-level settings. |