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..
On the Integration of CP-nets in ASPRIN
Authors: Mario Alviano, Javier Romero, Torsten Schaub
IJCAI 2019 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | The different solving techniques for CP-nets available in ASPRIN are evaluated empirically on testcases generated from the 193 instances of [Brewka et al., 2015b]. We measured the time to compute one optimal answer set with up to five techniques: LOOPS, the algorithm in Section 5; USC, the approximation (Section 4) provided by weak constraints using unsatisfiable core analysis (--opt-strat=usc option); HEUR, the approximation provided by lexicographic compositions of subset preferences using domain heuristics (#heuristic directives); PL-SQ and PL-LIN, the algorithm in Section 3 respectively with quadratic and linear diameter. Experiments ran on an Intel Xeon 2.20GHz processor under Linux, and resources were limited to 3600 seconds of runtime and 8 GB of memory. Testcases and details can be downloaded at https://potassco.org/asprin/. Experimental results are shown in Figure 5. |
| Researcher Affiliation | Academia | 1University of Calabria, Italy 2University of Potsdam, Germany |
| Pseudocode | Yes | Algorithm 1: TREEDT(N, I, I ); Algorithm 2: TREEDT-restated(N, I, I ); Figure 3: Preference program ΠCP for (possibly cyclic) CP-nets; Figure 4: Preference program for tree-shaped CP-nets |
| Open Source Code | Yes | Testcases and details can be downloaded at https://potassco.org/asprin/. |
| Open Datasets | Yes | The different solving techniques for CP-nets available in ASPRIN are evaluated empirically on testcases generated from the 193 instances of [Brewka et al., 2015b]. |
| Dataset Splits | No | The paper mentions "testcases" and discusses performance on them but does not specify any training, validation, or test dataset splits (e.g., percentages or specific counts for each split). |
| Hardware Specification | Yes | Experiments ran on an Intel Xeon 2.20GHz processor under Linux, and resources were limited to 3600 seconds of runtime and 8 GB of memory. |
| Software Dependencies | No | The paper mentions ASPRIN and ASP solvers (e.g., clasp in related work context) but does not provide specific version numbers for these or other software dependencies. |
| Experiment Setup | Yes | Experiments ran on an Intel Xeon 2.20GHz processor under Linux, and resources were limited to 3600 seconds of runtime and 8 GB of memory. |