Exact ASP Counting with Compact Encodings
Authors: Mohimenul Kabir, Supratik Chakraborty, Kuldeep S. Meel
AAAI 2024 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Our extensive empirical analysis over 1470 benchmarks demonstrates significant performance gain over current state-of-the-art exact answer set counters. |
| Researcher Affiliation | Academia | Mohimenul Kabir1, Supratik Chakraborty2, Kuldeep S Meel3 1National University of Singapore 2Indian Institute of Technology Bombay 3University of Toronto |
| Pseudocode | Yes | Algorithm 1: sharp ASP(P) |
| Open Source Code | Yes | Available at https://github.com/meelgroup/sharpASP |
| Open Datasets | Yes | Our benchmark suite consists of non-tight programs from the domains of the Hamiltonian cycle and graph reachability problems (Kabir et al. 2022; Aziz et al. 2015). We also considered the benchmark set from (Eiter, Hecher, and Kiesel 2021) (designated as aspben). |
| Dataset Splits | No | The paper does not provide specific details about training, validation, or test dataset splits. It mentions using 'benchmarks' but no proportions or counts for splitting. |
| Hardware Specification | Yes | We ran experiments on a high-performance computer cluster, where each node consists of AMD EPYC 7713 CPUs running with 128 real cores. |
| Software Dependencies | No | The paper mentions using 'GANAK, D4, and Sharp SAT-TD' as underlying model counters but does not specify their version numbers or any other software dependencies with version information. |
| Experiment Setup | No | The paper mentions general experimental limits ('runtime and memory limit were set to 5000 seconds and 8GB') and a threshold for the hybrid counter ('maximum of 10^5 answer sets'), but it does not provide specific hyperparameters or system-level training settings like learning rates, batch sizes, or optimizer configurations. |