Notice: This website is continuously updated as new conference proceedings and journal volumes are published. Consequently, the figures presented here may differ from those reported in our publications.
A brief overview of our methodology is provided on the Methods page, along with references to publications documenting our approach and links to the code and data used in each study.
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.
Checking the Consistency of Combined Qualitative Constraint Networks
Authors: Quentin Cohen-Solal, Maroua Bouzid, Alexandre Niveau
AAAI 2017 | Venue PDF | LLM Run Details | Input Tokens: 16,067 Total number of tokens sent to the LLM as input for this paper's analysis. | Output Tokens: 2,915 Total number of tokens produced by the LLM (including reasoning/thinking tokens) for this paper's analysis.
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Theoretical | We study the problem of consistency checking for constraint networks over combined qualitative formalisms. We propose a framework which encompasses loose integrations and a form of spatio-temporal reasoning. In particular, we identify sufficient conditions ensuring the polynomiality of consistency checking, and we use them to find tractable subclasses. The most notable results of this paper are two complementary theorems entailing the tractability of consistency checking. |
| Researcher Affiliation | Academia | Quentin Cohen-Solal, Maroua Bouzid, Alexandre Niveau Normandie Univ, UNICAEN, ENSICAEN, CNRS, GREYC, 14000 Caen, France EMAIL |
| Pseudocode | No | The paper describes algorithmic procedures (e.g., for algebraic closure) in prose, but it does not present any structured pseudocode or algorithm blocks. |
| Open Source Code | No | The paper does not provide any statement or link indicating that source code for the described methodology is publicly available. |
| Open Datasets | No | The paper focuses on theoretical contributions and does not conduct experiments involving datasets for training or evaluation. |
| Dataset Splits | No | The paper focuses on theoretical contributions and does not conduct experiments involving data splits for training, validation, or testing. |
| Hardware Specification | No | The paper is theoretical and does not report on experiments, thus no hardware specifications are mentioned. |
| Software Dependencies | No | The paper is theoretical and does not report on experiments, thus no specific software dependencies with version numbers are mentioned. |
| Experiment Setup | No | The paper is theoretical and does not report on empirical experiments; therefore, it does not provide details about an experimental setup. |