Stable Model Counting and Its Application in Probabilistic Logic Programming
Authors: Rehan Aziz, Geoffrey Chu, Christian Muise, Peter Stuckey
AAAI 2015 | Conference PDF | Archive PDF | Plain Text | 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. |