Acceptability Semantics for Weighted Argumentation Frameworks
Authors: Leila Amgoud, Jonathan Ben-Naim, Dragan Doder, Srdjan Vesic
IJCAI 2017 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Theoretical | The paper studies semantics that evaluate arguments in argumentation graphs, where each argument has a basic strength, and may be attacked by other arguments. It starts by defining a set of principles, each of which is a property that a semantics could satisfy. It provides the first formal analysis and comparison of existing semantics. Finally, it defines three novel semantics that satisfy more principles than existing ones. |
| Researcher Affiliation | Academia | 1 IRIT, CNRS Universit e de Toulouse, France 2 Faculty of Mechanical Engineering, University of Belgrade, Serbia 3 CRIL, CNRS Universit e d Artois, France |
| Pseudocode | No | The paper defines mathematical functions (e.g., Definition 4 (fm), Definition 7 (fc), Definition 9 (fh)) that describe the semantics, but it does not include explicitly labeled pseudocode or algorithm blocks. |
| Open Source Code | No | The paper states, "We implemented the three semantics from this section." but does not provide a link to the source code or explicitly state that it is open source. |
| Open Datasets | No | The paper is theoretical and focuses on formal analysis and definition of semantics. It uses small, illustrative examples (like WAG G1, G2, G3) rather than empirical datasets for training or evaluation, and thus provides no information on publicly available datasets. |
| Dataset Splits | No | The paper is theoretical and does not involve empirical experiments requiring dataset splits for training, validation, or testing. |
| Hardware Specification | No | The paper states, "We implemented the three semantics from this section. The degrees can be calculated in 1 or 2 seconds even for complex/large graphs." However, it does not provide any specific details about the hardware used (e.g., GPU/CPU models, memory). |
| Software Dependencies | No | The paper does not mention any specific software dependencies with version numbers. |
| Experiment Setup | No | The paper is theoretical and does not describe an empirical experimental setup with hyperparameters or system-level training settings. |