Body-Decoupled Grounding via Solving: A Novel Approach on the ASP Bottleneck
Authors: Viktor Besin, Markus Hecher, Stefan Woltran
IJCAI 2022 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We demonstrate the feasibility of this new method experimentally by comparing it to standard ASP technology in terms of grounding size, grounding time and total runtime. |
| Researcher Affiliation | Academia | 1TU Wien, Vienna, Austria 2University of Potsdam, Potsdam, Germany |
| Pseudocode | Yes | Figure 1: Body-decoupled grounding procedure R for a given tight non-ground program Π, which creates a disjunctive ground program. |
| Open Source Code | Yes | 1Our system including supplemental material of this work is publicly available at https://github.com/viktorbesin/newground. |
| Open Datasets | Yes | For answering the hypotheses, we use crafted (random) and applicable ASP competition instances. Note that competition instances are not designed to run into grounding bottlenecks. |
| Dataset Splits | No | The paper describes generating instances and using ASP competition instances but does not specify any explicit train/validation/test dataset splits, percentages, or cross-validation methods. |
| Hardware Specification | No | The paper states: "We limit main memory (RAM) to 16GB and overall runtimes (grounding & solving) to 1800s." This mentions memory limits but does not specify CPU models, GPU models, or other detailed hardware components used for running the experiments. |
| Software Dependencies | Yes | The system newground is written in Python3 and uses, among others, the API and of clingo 5.5 and its ability to efficiently parse logic programs via syntax trees. |
| Experiment Setup | Yes | We limit main memory (RAM) to 16GB and overall runtimes (grounding & solving) to 1800s. |