Parametrized Gradual Semantics Dealing with Varied Degrees of Compensation
Authors: Dragan Doder, Leila Amgoud, Srdjan Vesic
IJCAI 2023 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Theoretical | This paper proposes a parameterised family of gradual semantics, which unifies multiple semantics that share some principles but differ in their strategy regarding solving dilemmas. Indeed, we show that the two semantics taking the extreme values of the parameter favour respectively quantity and quality, while all the remaining ones compensate at some degree. We define three classes of compensation degrees and show that the novel family is able to compensate at all of them while none of the existing gradual semantics does. ... All proofs are provided in supplementary material. |
| Researcher Affiliation | Academia | Dragan Doder1 , Leila Amgoud2 and Srdjan Vesic3 1Utrecht University 2CNRS IRIT 3CRIL CNRS Univ. Artois d.doder@uu.nl, leila.amgoud@irit.fr, vesic@cril.fr |
| Pseudocode | No | The paper provides mathematical definitions and formulas (e.g., Definition 3 and equation (1)) for its proposed semantics, which describe a computational process. However, these are not presented or labeled as structured pseudocode or algorithm blocks. |
| Open Source Code | No | The paper does not provide any statement or link regarding the availability of open-source code for the described methodology. |
| Open Datasets | No | The paper uses illustrative 'argumentation graphs' (G1, G2, G3) for theoretical examples and calculations, but it does not utilize or refer to any publicly available or open datasets for training or empirical evaluation in the sense of machine learning or data-driven research. |
| Dataset Splits | No | The paper is theoretical and does not involve empirical experiments with datasets. Therefore, it does not provide training/test/validation dataset splits. |
| Hardware Specification | No | The paper does not describe any specific hardware used to run experiments. It is a theoretical paper focusing on semantics definitions and proofs. |
| Software Dependencies | No | The paper does not provide any specific software dependencies or their version numbers. It is a theoretical paper. |
| Experiment Setup | No | The paper is theoretical and does not involve empirical experiments. Therefore, it does not provide details about an experimental setup, hyperparameters, or system-level training settings. |