AMO-aware Aggregates in Answer Set Programming
Authors: Mario Alviano, Carmine Dodaro, Salvatore Fiorentino, Marco Maratea
IJCAI 2024 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We implemented the proposed propagator and assessed it empirically, reporting performance gain of several orders of magnitude. The implemented system, referred to as AMOWASP, was assessed empirically against the plain version of WASP [Alviano et al., 2015] v. f3e4c56 and the state-of-the-art system CLINGO v. 5.4.0 [Gebser et al., 2016]. |
| Researcher Affiliation | Academia | Mario Alviano , Carmine Dodaro , Salvatore Fiorentino and Marco Maratea Department of Mathematics and Computer Science, University of Calabria, Italy {mario.alviano, carmine.dodaro, marco.maratea}@unical.it, fiorentinosalvatore65@gmail.com |
| Pseudocode | No | The paper does not contain structured pseudocode or algorithm blocks. |
| Open Source Code | Yes | Finally, we remark here that all the material required to replicate the experiments are available at https://zenodo.org/records/11115982 [Alviano et al., 2024]. |
| Open Datasets | Yes | Instances are generated from those employed in the ASP competition [Calimeri et al., 2016] |
| Dataset Splits | No | The paper describes problem instances (Synthetic Benchmark, Graph Coloring, Knapsack) but does not provide specific training, validation, and test dataset splits, as these are combinatorial problems often solved on full instances rather than using traditional data splits. |
| Hardware Specification | Yes | Experiments were executed on an Intel Xeon 2.4 GHz server with 16 GB of memory. |
| Software Dependencies | Yes | The implemented system, referred to as AMOWASP, was assessed empirically against the plain version of WASP [Alviano et al., 2015] v. f3e4c56 and the state-of-the-art system CLINGO v. 5.4.0 [Gebser et al., 2016]. All the tested systems use GRINGO (included in the binary of CLINGO) as grounder. |
| Experiment Setup | Yes | Synthetic Benchmark (SB). ...The partition comprises 10 parts of uniform size part size {10, 100, 1000}, with the i-th literal of each part having weight i. The bound of the SUM is set to α C1 (achievable) and C1+α (C2 C1) (unachievable), where: α {0.15, 0.45, 0.6, 0.9}. Knapsack (K). ...The number n of types varies from 10 to 55 with an increasing step of 5. The maximum number of items that can be selected for each type is fixed to k = 20. |