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].
Combining DL-Lite_{bool}^N with Branching Time: A gentle Marriage
Authors: Víctor Gutiérrez-Basulto, Jean Christoph Jung
IJCAI 2017 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Theoretical | For the resulting logics, we present algorithms for the satisfiability problem and (mostly tight) complexity bounds ranging from EXPTIME to 3EXPTIME. |
| Researcher Affiliation | Academia | V ıctor Guti errez-Basulto Cardiff University, UK EMAIL Jean Christoph Jung Universit at Bremen, Germany EMAIL |
| Pseudocode | No | The paper describes the algorithms and methods in textual form but does not provide structured pseudocode or algorithm blocks. |
| Open Source Code | No | The paper provides a link to an extended version with an appendix (http://tinyurl.com/ktkgwqg), but it does not state that source code for the described methodology is released or available. |
| Open Datasets | No | The paper is theoretical and does not involve empirical experiments with datasets, thus no dataset usage or public availability information is provided. |
| Dataset Splits | No | The paper is theoretical and does not involve empirical experiments with datasets, thus no training/test/validation splits are described. |
| Hardware Specification | No | The paper focuses on theoretical contributions (algorithms and complexity bounds) and does not describe empirical experiments, therefore no specific hardware specifications are mentioned. |
| Software Dependencies | No | The paper focuses on theoretical contributions and does not describe empirical experiments. Therefore, no specific software dependencies with version numbers for replication are mentioned. |
| Experiment Setup | No | The paper focuses on theoretical contributions and does not describe empirical experiments. Therefore, no specific experimental setup details like hyperparameters or training configurations are provided. |