Counting Maximal Satisfiable Subsets
Authors: Jaroslav BendÃk, Kuldeep S. Meel3651-3660
AAAI 2021 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Our empirical evaluation demonstrates that our approach can scale to instances clearly beyond the reach of enumeration-based techniques. To demonstrate the empirical effectiveness of our approach, we implemented a Python-based prototype and performed a detailed empirical analysis. |
| Researcher Affiliation | Academia | 1 Masaryk University, Brno, Czech Republic 2 National University of Singapore, Singapore |
| Pseudocode | No | The paper describes its algorithmic framework and components using propositional logic formulas and descriptions, but it does not include any explicitly labeled 'Pseudocode' or 'Algorithm' blocks. |
| Open Source Code | Yes | Our tool is publicly available at: https://github.com/jar-ben/MSSCounting |
| Open Datasets | Yes | We used a collection of 1200 Boolean CNF formulas that were recently used in prior MUS literature (Liu and Luo 2018; Luo and Liu 2019)3. ... 3https://github.com/luojie-sklsde/MUS Random Benchmarks |
| Dataset Splits | No | The paper describes using a collection of 1200 Boolean CNF formulas as 'benchmarks' for evaluation but does not specify any training, validation, or test dataset splits. |
| Hardware Specification | Yes | All experiments were run using a time limit of 3600 seconds (1 hour) and computed on an AMD EPYC 7371 16Core Processor, 1 TB memory machine running Debian Linux 4.19.67-2. |
| Software Dependencies | No | The paper mentions software like GANAK, UWr Max Sat, FLINT, RIME, and Python, but it does not provide specific version numbers for these components. |
| Experiment Setup | No | The paper specifies a time limit of 3600 seconds for experiments and mentions the backend tools used, but it does not provide specific hyperparameters, model initialization details, or training configurations typical of a machine learning experimental setup. |