Externally Supported Models for Efficient Computation of Paracoherent Answer Sets
Authors: Giovanni Amendola, Carmine Dodaro, Wolfgang Faber, Francesco Ricca
AAAI 2018 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | A performance comparison carried out on benchmarks from ASP competitions shows that the usage of the new transformation brings about performance improvements that are independent of the underlying algorithms. |
| Researcher Affiliation | Academia | Giovanni Amendola,1 Carmine Dodaro,2 Wolfgang Faber,3 Francesco Ricca1 1DEMACS, University of Calabria, Italy 2DIBRIS, University of Genova, Italy 3University of Huddersfield, UK |
| Pseudocode | No | The paper describes algorithms (MINIM, SPLIT, WEAK) in prose but does not provide any structured pseudocode or algorithm blocks. |
| Open Source Code | No | The paper states 'Algorithms described in the previous section were implemented using the state of the art ASP solver WASP (Alviano et al. 2015). In particular, we reused the variant of WASP presented in (Amendola et al. 2017).' This indicates usage of existing tools/variants, not release of code specific to this paper's contributions. |
| Open Datasets | Yes | We used exactly the same benchmark from (Amendola et al. 2017). ... The instances are from the latest ASP competition (Gebser, Maratea, and Ricca 2015)... |
| Dataset Splits | No | The paper mentions using 'benchmarks from ASP competitions' but does not specify any explicit train, validation, or test dataset splits, only referring to 'incoherent instances in the competition suite'. |
| Hardware Specification | Yes | Experiments were run on a system with 2.30GHz Intel Xeon E5-4610 v2 CPUs. |
| Software Dependencies | No | The paper mentions using 'WASP (Alviano et al. 2015)' but does not provide a specific version number for this or any other software dependency. |
| Experiment Setup | No | The paper mentions system constraints like 'Execution time and memory were limited to 1200 seconds and 8 GB, respectively,' but does not provide specific details on experimental setup such as hyperparameters, model initialization, or training schedules. |