Parallel Constraint Acquisition
Authors: Nadjib Lazaar3860-3867
AAAI 2021 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | In this section, we experimentally evaluate our portfolio-based parallel constraint acquisition system. |
| Researcher Affiliation | Academia | Nadjib Lazaar LIRMM, University of Montpellier, CNRS, Montpellier, France lazaar@lirmm.fr |
| Pseudocode | Yes | Algorithm 1: PACQ |
| Open Source Code | Yes | The code is publicly available at (gite.lirmm.fr/constraint-acquisition-team). |
| Open Datasets | Yes | Queens. (prob054 in CSPLib2) |
| Dataset Splits | No | The paper describes a learning process for constraint acquisition, but it does not specify any dedicated validation dataset splits or mention cross-validation for hyperparameter tuning or model selection. |
| Hardware Specification | Yes | All tests were conducted on an HPC node of 28 CPU cores and 128Gb of RAM. Each core is an Intel(R) Xeon(R) CPU E5-2640 v4 @2.40GHz. |
| Software Dependencies | Yes | The implementation of PACQ were carried out in Java using Choco solver 4.10.2. |
| Experiment Setup | Yes | The only parameter we will keep fixed in all our experiments is TL, that we set to 5 seconds as it corresponds to an acceptable waiting time for a human user (Lallemand and Gronier 2012). |