Stratified Evidence Logics
Authors: Philippe Balbiani, David Fernández-Duque, Andreas Herzig, Emiliano Lorini
IJCAI 2019 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Theoretical | In this paper we propose an extended framework which allows one to explicitly quantify either the number of evidence sets, or effort, needed to justify a given proposition, provide a complete deductive calculus and a proof of decidability, and show how existing frameworks can be embedded into ours. |
| Researcher Affiliation | Academia | 1Institut de Recherche en Informatique de Toulouse, Toulouse University 2Ghent University |
| Pseudocode | No | The paper does not contain any clearly labeled pseudocode blocks or algorithms. |
| Open Source Code | No | The paper does not provide any information about open-source code for the described methodology, nor does it include links to a code repository. |
| Open Datasets | No | The paper provides conceptual 'Motivating Examples' to illustrate the logical framework, but it does not mention or provide access information for any publicly available datasets used for empirical training. |
| Dataset Splits | No | The paper is theoretical and does not conduct experiments with datasets, thus no training/validation/test splits are discussed or provided. |
| Hardware Specification | No | The paper is theoretical and does not involve computational experiments that would require specific hardware specifications. |
| Software Dependencies | No | The paper focuses on theoretical development and does not describe computational experiments or specific software dependencies with version numbers. |
| Experiment Setup | No | The paper is theoretical and does not describe an experimental setup, hyperparameters, or training configurations. |