Notice: The reproducibility variables underlying each score are classified using an automated LLM-based pipeline, validated against a manually labeled dataset. LLM-based classification introduces uncertainty and potential bias; scores should be interpreted as estimates. Full accuracy metrics and methodology are described in [1].
Most Probable Explanation in Probabilistic Answer Set Programming
Authors: Damiano Azzolini, Giuseppe Mazzotta, Francesco Ricca, Fabrizio Riguzzi
IJCAI 2025 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | These approaches are implemented and evaluated against existing solvers across different datasets and configurations. Empirical results demonstrate that the novel solutions consistently outperform existing alternatives for non-stratified programs. |
| Researcher Affiliation | Academia | 1University of Ferrara, Ferrara, Italy 2University of Calabria, Rende, Italy |
| Pseudocode | No | The paper describes formal definitions and logic-based encodings but does not include any explicit pseudocode blocks or algorithms. |
| Open Source Code | Yes | Source code and datasets are available at https://t.ly/kqulc. |
| Open Datasets | Yes | Source code and datasets are available at https://t.ly/kqulc. |
| Dataset Splits | No | The paper describes the generation of datasets (e.g., 'randomly generated instances of increasing size', 'starting from 2 and up to 100') but does not provide specific details on how these datasets were split into training, validation, or test sets. |
| Hardware Specification | Yes | The experiments were executed on a machine running at 3.7 GHz with 32 GB of RAM and a time limit, for each instance, of 3600 seconds (1 hour). |
| Software Dependencies | No | The paper mentions several systems like PASTA, cplint, Prob Log, plingo, aspmc, and clingo, along with citations to their respective papers. However, it does not provide specific version numbers for these software components (e.g., 'clingo 5.x' or 'PASTA v1.0'). |
| Experiment Setup | Yes | The experiments were executed on a machine running at 3.7 GHz with 32 GB of RAM and a time limit, for each instance, of 3600 seconds (1 hour). In ASP and ASP(Q) implementations, we discretize log-probabilities as k log(p) and use k = 103. Moreover, for both ASP and plingo we used the clingo option --opt-strategy=usc [Andres et al., 2012]. |