Efficiently Explaining CSPs with Unsatisfiable Subset Optimization
Authors: Emilio Gamba, Bart Bogaerts, Tias Guns
IJCAI 2021 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We now experimentally validate the performance of the different versions of our algorithm. Our benchmarks were run on a compute cluster, where each explanation sequence generation was assigned a single core on a 10-core INTEL Xeon Gold 61482 (Skylake) processor, a timelimit of 120 minutes and a memory-limit of 4GB. |
| Researcher Affiliation | Academia | 1Vrije Universiteit Brussel, Belgium 2KU Leuven, Belgium |
| Pseudocode | Yes | Algorithm 1: EXPLAIN-ONE-STEP(C, f, I, Iend) |
| Open Source Code | Yes | Everything was implemented in Python on top of Py SAT1 and is available at https://github. com/ML-KULeuven/ocus-explain. |
| Open Datasets | Yes | All of our experiments were run on a direct translation to Py SAT of the 10 puzzles of Bogaerts et al. [2020]2. |
| Dataset Splits | No | No explicit mention of specific train/validation/test dataset splits, percentages, or counts for reproduction. |
| Hardware Specification | Yes | Our benchmarks were run on a compute cluster, where each explanation sequence generation was assigned a single core on a 10-core INTEL Xeon Gold 61482 (Skylake) processor, a timelimit of 120 minutes and a memory-limit of 4GB. |
| Software Dependencies | Yes | For MIP calls, we used Gurobi 9.0, for SAT calls Mini Sat 2.2 and for Max SAT calls RC2 as bundled with Py SAT (version 0.1.6.dev11). |
| Experiment Setup | Yes | We used a cost of 60 for puzzle-agnostic constraints; 100 for puzzle-speciļ¬c constraints; and cost 1 for facts. |