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..
Parallel Constraint Acquisition
Authors: Nadjib Lazaar3860-3867
AAAI 2021 | Venue PDF | 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 EMAIL |
| 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). |