Learning CP-net preferences online from user queries

Joshua T. Guerin, Thomas E. Allen, Judy Goldsmith

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

22 Scopus citations


We present an online, heuristic algorithm for learning Conditional Preference networks (CP-nets) from user queries. This is the first efficient and resolute CP-net learning algorithm: if a preference order can be represented as a CP-net, our algorithm learns a CP-net in time n p, where p is a bound on the number of parents a node may have. The learned CP-net is guaranteed to be consistent with the original CP-net on all queries from the learning process. We tested the algorithm on randomly generated CP-nets; the learned CP-nets agree with the originals on a high percent of non-training preference comparisons.

Original languageEnglish
Title of host publicationAlgorithmic Decision Theory - Third International Conference, ADT 2013, Proceedings
Number of pages13
StatePublished - 2013
Event3rd International Conference on Algorithmic Decision Theory, ADT 2013 - Bruxelles, Belgium
Duration: Nov 13 2013Nov 15 2013

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume8176 LNAI
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349


Conference3rd International Conference on Algorithmic Decision Theory, ADT 2013

ASJC Scopus subject areas

  • Theoretical Computer Science
  • General Computer Science


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