Bilateral Gradual Semantics for Weighted Argumentation
Authors: Zongshun Wang, Yuping Shen
AAAI 2024 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Theoretical | To this end, we first provide a set of principles for our semantics, taking both the acceptability and rejectability degrees into account, and propose three novel semantics conforming to the above principles. These semantics are defined as the limits of iterative sequences that always converge in any given weighted argumentation system, making them preferable for real-world applications. |
| Researcher Affiliation | Academia | Zongshun Wang, Yuping Shen Institute of Logic and Cognition, Department of Philosophy, Sun Yat-sen University, P.R. China wangzsh7@mail2.sysu.edu.cn, shyping@mail.sysu.edu.cn |
| Pseudocode | No | The paper defines iterative sequences using mathematical formulas (e.g., Definition 5), but these are not presented as structured pseudocode blocks or algorithms with numbered steps. |
| Open Source Code | No | The paper does not provide any statement about releasing source code or links to a code repository for the methodology described. |
| Open Datasets | No | The paper is theoretical and uses abstract Weighted Argumentation Graphs (WAG) for its definitions and proofs. It does not use any named, publicly available datasets for training or evaluation, nor does it provide any concrete access information for a dataset. |
| Dataset Splits | No | The paper is theoretical and does not involve empirical experiments with datasets; thus, no training/test/validation splits are discussed or provided. |
| Hardware Specification | No | The paper is theoretical and does not report on empirical experiments; therefore, no hardware specifications used for running experiments are mentioned. |
| Software Dependencies | No | The paper is theoretical and does not report on empirical experiments that would require specific software dependencies with version numbers. |
| Experiment Setup | No | The paper is theoretical and does not involve empirical experiments; therefore, no details about an experimental setup, such as hyperparameters or system-level training settings, are provided. |