Generating CP-Nets Uniformly at Random
Abstract
Conditional preference networks (CP-nets) are a commonly studied compact formalism for modeling preferences. To study the properties of CP-nets or the performance of CP-net algorithms on average, one needs to generate CP-nets in an equiprobable manner. We discuss common problems with naive generation, including sampling bias, which invalidates the base assumptions of many statistical tests and can undermine the results of an experimental study. 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.
Cite
Text
Allen et al. "Generating CP-Nets Uniformly at Random." AAAI Conference on Artificial Intelligence, 2016. doi:10.1609/AAAI.V30I1.10115Markdown
[Allen et al. "Generating CP-Nets Uniformly at Random." AAAI Conference on Artificial Intelligence, 2016.](https://mlanthology.org/aaai/2016/allen2016aaai-generating/) doi:10.1609/AAAI.V30I1.10115BibTeX
@inproceedings{allen2016aaai-generating,
title = {{Generating CP-Nets Uniformly at Random}},
author = {Allen, Thomas E. and Goldsmith, Judy and Justice, Hayden Elizabeth and Mattei, Nicholas and Raines, Kayla},
booktitle = {AAAI Conference on Artificial Intelligence},
year = {2016},
pages = {872-878},
doi = {10.1609/AAAI.V30I1.10115},
url = {https://mlanthology.org/aaai/2016/allen2016aaai-generating/}
}