TY - GEN
T1 - Generating CP-nets uniformly at random
AU - Allen, Thomas E.
AU - Goldsmith, Judy
AU - Justice, Hayden E.
AU - Mattei, Nicholas
AU - Raines, Kayla
PY - 2016
Y1 - 2016
N2 - 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 näive 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.
AB - 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 näive 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.
UR - http://www.scopus.com/inward/record.url?scp=85007180297&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=85007180297&partnerID=8YFLogxK
M3 - Conference contribution
AN - SCOPUS:85007180297
T3 - 30th AAAI Conference on Artificial Intelligence, AAAI 2016
SP - 872
EP - 878
BT - 30th AAAI Conference on Artificial Intelligence, AAAI 2016
T2 - 30th AAAI Conference on Artificial Intelligence, AAAI 2016
Y2 - 12 February 2016 through 17 February 2016
ER -