Generating CP-Nets Uniformly at Random
Authors: Thomas Allen, Judy Goldsmith, Hayden Justice, Nicholas Mattei, Kayla Raines
AAAI 2016 | Venue PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We provide a novel algorithm for provably generating acyclic CP-nets uniformly at random. Our method is computationally efficient and allows for multi-valued domains and arbitrary bounds on the indegree in the dependency graph. [...] We have implemented our method in C++ using the Gnu MP library (Granlund et al. 2014), allowing generation of thousands of CP-nets per second. |
| Researcher Affiliation | Academia | Thomas E. Allen University of Kentucky Lexington, Kentucky, USA EMAIL Judy Goldsmith University of Kentucky Lexington, Kentucky, USA EMAIL Hayden E. Justice The Gatton Academy, WKU Bowling Green, Kentucky, USA EMAIL Nicholas Mattei Data61 and UNSW Sydney, Australia EMAIL Kayla Raines University of Kentucky Lexington, Kentucky, USA EMAIL |
| Pseudocode | Yes | Algorithm 1: DAGCODE-TO-DAG [...] Algorithm 2: ALL-DAGS [...] Algorithm 3: BUILD-CP-NET [...] Algorithm 4: ALL-CP-NETS [...] Algorithm 5: COMPUTE-DISTRIBUTION [...] Algorithm 6: RANDOM-CP-NET |
| Open Source Code | Yes | Our code is available at http://cs.uky.edu/ goldsmit/papers/ Generating CPnet Code.html. |
| Open Datasets | No | The paper discusses generating CP-nets and their properties, but does not describe experiments involving training datasets for models or algorithms. |
| Dataset Splits | No | The paper does not explicitly provide validation dataset splits. Its focus is on a generation algorithm, not empirical model training. |
| Hardware Specification | No | The paper states, 'We have implemented our method in C++ [...] allowing generation of thousands of CP-nets per second,' but does not specify the hardware used for this implementation or performance measurement. |
| Software Dependencies | Yes | We have implemented our method in C++ using the Gnu MP library (Granlund et al. 2014). |
| Experiment Setup | No | The paper describes its algorithm for generating CP-nets but does not detail an experimental setup with specific hyperparameters or system-level training settings, as it is not training a machine learning model. |