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 Coakley et alK. L. Coakley, T. Snelleman, H. Hoos, and O. E. Gundersen, "The embrace of open science: An analysis of a decade of AI research and 56 800 conference papers," Under Review, 2026..
Stable Model Counting and Its Application in Probabilistic Logic Programming
Authors: Rehan Aziz, Geoffrey Chu, Christian Muise, Peter Stuckey
AAAI 2015 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | 5 Experiments We compare the two approaches based on implementation of unfounded set detection as explained in Section 3 against the proof based encoding of PROBLOG2. We use two well-studied benchmarks: Smokers Friends (Fierens et al. 2011) problem and the graph reliability problem (Graph Rel) (Arora and Barak 2009) with evidence constraints. |
| Researcher Affiliation | Collaboration | Rehan Abdul Aziz, Geoffrey Chu, Christian Muise and Peter Stuckey National ICT Australia, Victoria Laboratory Department of Computing and Information Systems The University of Melbourne |
| Pseudocode | No | Explanation: The paper describes methods and techniques but does not include structured pseudocode or algorithm blocks. |
| Open Source Code | No | Explanation: The paper does not provide an explicit statement or link indicating that the source code for their methodology is publicly available. |
| Open Datasets | Yes | We use two well-studied benchmarks: Smokers Friends (Fierens et al. 2011) problem and the graph reliability problem (Graph Rel) (Arora and Barak 2009) with evidence constraints. |
| Dataset Splits | No | Explanation: The paper mentions using benchmarks but does not provide specific details on training, validation, and test dataset splits. |
| Hardware Specification | Yes | All experiments were run on a machine running Ubuntu 12.04.1 LTS with 8 GB of physical memory and Intel(R) Core(TM) i7-2600 3.4 GHz processor. |
| Software Dependencies | No | Explanation: The paper mentions software like Ubuntu, PROBLOG2, DSHARP, and SHARPSAT, but it does not provide specific version numbers for these or other relevant software dependencies. |
| Experiment Setup | No | Explanation: The paper describes the experimental comparison of different approaches but does not provide specific details about experimental setup such as hyperparameter values, training configurations, or system-level settings. |