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 Coakley et alK. L. Coakley, T. Snelleman, H. Hoos, and O. E. Gundersen, "The embrace of open science: An analysis of a decade of AI research and 56 800 conference papers," Under Review, 2026..
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. |