Notice: The reproducibility variables underlying each score are classified using an automated LLM-based pipeline, validated against a manually labeled dataset. LLM-based classification introduces uncertainty and potential bias; scores should be interpreted as estimates. Full accuracy metrics and methodology are described in [1].
Safe Inductions: An Algebraic Study
Authors: Bart Bogaerts, Joost Vennekens, Marc Denecker
IJCAI 2017 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Theoretical | In this paper, we formally deο¬ne the safety criterion algebraically. We study properties of so-called safe inductions and apply our theory to logic programming and autoepistemic logic. |
| Researcher Affiliation | Academia | Bart Bogaerts and Joost Vennekens and Marc Denecker KU Leuven, Department of Computer Science Celestijnenlaan 200A, Leuven, Belgium |
| Pseudocode | No | The paper does not contain structured pseudocode or algorithm blocks. |
| Open Source Code | No | The paper does not provide concrete access to source code for the methodology described. |
| Open Datasets | No | The paper is theoretical and does not use datasets for training or experimentation, therefore no access information for such datasets is provided. |
| Dataset Splits | No | The paper is theoretical and does not describe empirical experiments involving dataset splits. |
| Hardware Specification | No | The paper is theoretical and does not describe empirical experiments, thus no hardware specifications are provided. |
| Software Dependencies | No | The paper is theoretical and focuses on abstract algebraic theory, not on specific software implementations or their versions. |
| Experiment Setup | No | The paper is theoretical and does not describe empirical experiments, thus no experimental setup details like hyperparameters or training configurations are provided. |