Bypassing the ASP Bottleneck: Hybrid Grounding by Splitting and Rewriting
Authors: Alexander Beiser, Markus Hecher, Kaan Unalan, Stefan Woltran
IJCAI 2024 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Our experimental results indicate that this approach is competitive, especially for instances where traditional grounding fails. ... We compared Na GG against state-of-the-art grounders gringo 5.6.2 and idlv 1.1.6. We compare four experimental setups, gringo, idlv, Na GG-gringo, and idlvgringo. ... We confirm H1 via Table 1 and Figure 2. On application instances gringo is only capable of grounding and solving 26 instances of S1, which is increased to 50 by Na GG-gringo. |
| Researcher Affiliation | Academia | 1TU Wien, Vienna, Austria 2Massachusetts Institute of Technology, Cambridge, MA, United States |
| Pseudocode | Yes | Figure 1: Hybrid grounding procedure H(Πc, Πt), which creates a disjunctive ground program from a given non-ground HCF program Πc Πt such that Πt is tight. We thereby interleave classical grounding on Πc with body-decoupled grounding on Πt. (The figure contains a block of rules (2)-(12) formatted like pseudocode or an algorithm.) |
| Open Source Code | Yes | Supplementary material including source code, benchmark instances and experimental results, are available online: https://github.com/alexl4123/newground/releases/tag/v2.0.0. |
| Open Datasets | Yes | Supplementary material including source code, benchmark instances and experimental results, are available online: https://github.com/alexl4123/newground/releases/tag/v2.0.0. ... For S1, we take instances from the ASP competition (2014). We generate graphs with varying instance density (edge probability) and instance size for S2, S3 and S4... |
| Dataset Splits | No | The paper refers to different benchmark scenarios and generated instances for evaluation, but does not specify a training, validation, and test split for these datasets in the typical machine learning sense. |
| Hardware Specification | Yes | We used a benchmark system with an AMD Opteron 6272 with 225GB RAM on Debian10 with kernel 4.19.0-16-amd64. |
| Software Dependencies | Yes | Our prototypical grounder is based on Python3 and the clingo 5.6 API. ... We compared Na GG against state-of-the-art grounders gringo 5.6.2 and idlv 1.1.6. |
| Experiment Setup | Yes | In our benchmarks, we limit available main memory (RAM) to 32GB (for each grounding or solving), and the overall runtime for both grounding and solving to 1800s. ... For S3 and S4 we model different rule densities k, i.e., number of different body variables, allowing us to study rule scalability. |