Ranking Extensions in Abstract Argumentation
Authors: Kenneth Skiba, Tjitze Rienstra, Matthias Thimm, Jesse Heyninck, Gabriele Kern-Isberner
IJCAI 2021 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Theoretical | In this paper, we present the notion of extension-ranking semantics, which determines a preordering over sets of arguments, where one set is deemed more plausible than another if it is somehow more acceptable. We obtain extension-based semantics as a special case of this new approach, but it also allows us to make more fine-grained distinctions... We define a number of general principles to classify extension-ranking semantics and develop concrete approaches. We also study the relation between extension-ranking semantics and argument-ranking semantics... |
| Researcher Affiliation | Academia | 1University of Koblenz-Landau, Koblenz, Germany 2TU Dortmund, Dortmund, Germany |
| Pseudocode | No | The paper does not contain structured pseudocode or algorithm blocks. Descriptions of functions and semantics are provided in mathematical notation and natural language. |
| Open Source Code | No | The paper does not provide any concrete access to source code for the methodology described. There is no mention of a repository link or an explicit code release statement. |
| Open Datasets | No | The paper is theoretical and uses abstract argumentation frameworks (e.g., AF1, AF2, AF3) for illustrative examples, which are defined within the text. It does not mention or use any publicly available datasets for training, validation, or testing. |
| Dataset Splits | No | The paper is theoretical and uses illustrative examples rather than empirical data, therefore it does not provide specific dataset split information (e.g., training, validation, test splits) needed to reproduce data partitioning. |
| Hardware Specification | No | The paper is a theoretical work focusing on abstract argumentation semantics. It does not describe any computational experiments or provide specific hardware details used for running experiments. |
| Software Dependencies | No | The paper is theoretical and does not mention any software dependencies with specific version numbers for replication. |
| Experiment Setup | No | The paper is theoretical and does not describe an experimental setup with specific hyperparameters, training configurations, or system-level settings. |