Generating CP-Nets Uniformly at Random
Authors: Thomas Allen, Judy Goldsmith, Hayden Justice, Nicholas Mattei, Kayla Raines
AAAI 2016 | Conference 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 teal223@g.uky.edu Judy Goldsmith University of Kentucky Lexington, Kentucky, USA goldsmit@cs.uky.edu Hayden E. Justice The Gatton Academy, WKU Bowling Green, Kentucky, USA hayden.justice259@topper.wku.edu Nicholas Mattei Data61 and UNSW Sydney, Australia nicholas.mattei@nicta.com.au Kayla Raines University of Kentucky Lexington, Kentucky, USA kayla.raines@live.com |
| 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. |