ApproxASP – a Scalable Approximate Answer Set Counter

Authors: Mohimenul Kabir, Flavio O Everardo, Ankit K Shukla, Markus Hecher, Johannes Klaus Fichte, Kuldeep S Meel5755-5764

AAAI 2022 | Conference PDF | Archive PDF | Plain Text | LLM Run Details

Reproducibility Variable Result LLM Response
Research Type Experimental Finally, our experimental evaluation shows the scalability of our approach over existing ASP systems.
Researcher Affiliation Academia 1 National University of Singapore, Singapore 2 Tec de Monterrey Campus Puebla, Mexico 3 JKU, Linz, Austria 4 TU Wien, Vienna, Austria
Pseudocode Yes Listing 1: Approximate Counting (Approx ASP) Data: Program P, Independent support I, tolerance ε, confidence δ Result: Approximate number of answer sets
Open Source Code No No explicit statement or link providing access to the open-source code for Approx ASP was found.
Open Datasets No We randomly generated 1500 graphs, consisting of at most 50 vertices and 250 edges taken equally from each class. For disjunctive programs (ii), we use the projected model counting problem on 2QBFs, which is known to be # co-NP-complete (Durand, Hermann, and Kolaitis 2005). We generated 200 random instances, where the number of variables and clauses are at most 100 and 200, respectively.
Dataset Splits No No explicit statement about training, validation, or test dataset splits was found.
Hardware Specification Yes All experiments were carried out on a high-performance computer cluster, where each node consists of an 2x E5-2690v3 CPUs running with 2x12 real cores and 96GB of RAM.
Software Dependencies Yes We selected four programs that allow for counting answer sets, namely, Dyn ASP v2.0 (Fichte et al. 2017), clingo v5.4.0 (Gebser et al. 2007), Ganak (Sharma et al. 2019), Approx MC4 (Soos, Gocht, and Meel 2020).
Experiment Setup Yes In line with previous works in approximate counting, we set ε = 0.8 and δ = 0.2 for both Approx MC and Approx ASP.