Learning Constraint Networks over Unknown Constraint Languages

Authors: Christian Bessiere, Clément Carbonnel, Areski Himeur

IJCAI 2023 | Conference PDF | Archive PDF | Plain Text | 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 {bessiere, clement.carbonnel, areski.himeur}@lirmm.fr
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.