A General Setting for Gradual Semantics Dealing with Similarity
Authors: Leila Amgoud, Victor David6185-6192
AAAI 2021 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Theoretical | This paper proposes such theoretical foundations. Rather than focus narrowly on a particular semantics, we propose a general setting for deļ¬ning systematically semantics that consider similarities. |
| Researcher Affiliation | Academia | 1 IRIT CNRS ANITI, Toulouse University, France 2 IRIT CNRS, Toulouse University, France |
| Pseudocode | No | The paper defines functions and properties mathematically and textually, but it does not include any structured pseudocode or algorithm blocks. |
| Open Source Code | No | The paper does not provide any information or links regarding the availability of open-source code for the described methodology. |
| Open Datasets | No | The paper focuses on theoretical foundations and does not describe experiments using specific datasets. Therefore, it does not provide concrete access information for any publicly available or open dataset for training. |
| Dataset Splits | No | The paper is theoretical and does not involve empirical experiments or data. Thus, it does not specify any training/validation/test dataset splits. |
| Hardware Specification | No | The paper is theoretical and does not describe empirical experiments. Therefore, no hardware specifications for running experiments are mentioned. |
| Software Dependencies | No | The paper is theoretical and focuses on mathematical definitions and properties. It does not mention any specific software dependencies or their version numbers for implementation or experimentation. |
| Experiment Setup | No | The paper is theoretical and does not conduct empirical experiments. Thus, there are no specific details about an experimental setup, such as hyperparameters or training configurations. |