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].
Merging in the Horn Fragment
Authors: Adrian Haret, Stefan Rümmele, Stefan Woltran
IJCAI 2015 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Theoretical | In this paper, we provide a novel representation theorem for Horn merging by strengthening the standard merging postulates. Moreover, we present a concrete Horn merging operator satisfying all postulates. Due to lack of space, we do not include here the proofs of the claims found in the text. |
| Researcher Affiliation | Academia | Adrian Haret, Stefan R ummele, Stefan Woltran EMAIL Institute of Information Systems Vienna University of Technology, Austria |
| Pseudocode | No | The paper does not contain any pseudocode or algorithm blocks. |
| Open Source Code | No | The paper does not provide any information about open-source code for the methodology described. |
| Open Datasets | No | The paper is theoretical and does not conduct experiments on datasets, therefore no dataset access information is provided. |
| Dataset Splits | No | The paper is theoretical and does not involve empirical experiments, thus no training/validation/test splits are mentioned. |
| Hardware Specification | No | The paper is theoretical and does not describe any hardware specifications for running experiments. |
| Software Dependencies | No | The paper is theoretical and does not provide specific software dependencies or version numbers. |
| Experiment Setup | No | The paper is theoretical and does not describe any experimental setup details such as hyperparameters or training settings. |