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..
Learning Constraint Networks over Unknown Constraint Languages
Authors: Christian Bessiere, Clément Carbonnel, Areski Himeur
IJCAI 2023 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We report preliminary experiments on various acquisition benchmarks. |
| Researcher Affiliation | Academia | Christian Bessiere , Cl ement Carbonnel , Areski Himeur University of Montpellier, CNRS, LIRMM, Montpellier, France EMAIL |
| Pseudocode | No | The paper describes its method and model using prose and mathematical formulas, but it does not contain any structured pseudocode or algorithm blocks. |
| Open Source Code | Yes | Code and data required for conducting the experiments are available at https://gite.lirmm.fr/coconut/language-free-acq |
| Open Datasets | Yes | Code and data required for conducting the experiments are available at https://gite.lirmm.fr/coconut/language-free-acq |
| Dataset Splits | No | The paper describes using 'training sets' and evaluating 'accuracy on a new set of 2000 examples generated independently', but it does not explicitly define or mention a separate 'validation' split or specific train/validation/test percentages. |
| Hardware Specification | Yes | All experiments1 are run on one core of an Intel Xeon E5-2680 v4 2.4GHz processor with 8GB of memory. |
| Software Dependencies | No | The paper mentions implementing the strategy in 'the Python programming language' and using the 'UWRMAXSAT solver', but it does not specify version numbers for either Python or UWRMAXSAT. |
| Experiment Setup | Yes | We conduct a series of experiments with different numbers of examples in the training sets. For each benchmark problem and number |E| of examples, we run our acquisition method 5 times with a new randomly sampled training set for each run. Training sets contain positive and negative examples in the same proportion. The timeout is set to 12 hours. |