An Approximate Skolem Function Counter

Authors: Arijit Shaw, Brendan Juba, Kuldeep S. Meel

AAAI 2024 | Conference PDF | Archive PDF | Plain Text | LLM Run Details

Reproducibility Variable Result LLM Response
Research Type Experimental We conducted a thorough evaluation of the performance and accuracy of results of the Skolem FC algorithm by implementing a functional prototype in C++.
Researcher Affiliation Academia Arijit Shaw1,2, Brendan Juba3, Kuldeep S. Meel4 1 Chennai Mathematical Institute, India 2 IAI, TCG-CREST, Kolkata, India 3 Washington University in St. Louis, USA 4 University of Toronto, Canada
Pseudocode Yes Algorithm 1: Stopping Rule (ε, δ), Algorithm 2: Skolem FC(F(X, Y ), ε, δ), Algorithm 3: Baseline(F(X, Y ))
Open Source Code Yes Source code: https://github.com/meelgroup/skolemfc/
Open Datasets Yes All benchmarks and experimental data are available at https://doi.org/10.5281/zenodo.10404174
Dataset Splits No The paper mentions using two sets of benchmarks (609 efficiency instances and 158 correctness instances) but does not specify how these instances are split into training, validation, or test sets.
Hardware Specification Yes All experiments were carried out on a cluster of nodes consisting of AMD EPYC 7713 CPUs running with 2x64 real cores.
Software Dependencies No The paper mentions using 'Uni Samp', 'Approx MC', 'Ganak (latest version)', and 'Crypto Mini Sat' but does not provide specific version numbers for these software dependencies.
Experiment Setup Yes Skolem FC was tested with ε = 0.8 and δ = 0.4. That gave the following values to error and tolerance parameters for model counting and sampling oracles. The almost uniform sampling oracle Uni Samp is run with εs = 0.16. The approximate model counting oracle Approx MC in line 7 was run with εc = 4 and δc = 0.32/ms, where s comes from the algorithm, based on input (ε, δ) and m is number of output variables in the specification.