Preference learning and optimization for partial lexicographic preference forests over combinatorial domains

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1 Scopus citations


We study preference representation models based on partial lexicographic preference trees (PLP-trees). We propose to represent preference relations as forests of small PLP-trees (PLP-forests), and to use voting rules to aggregate orders represented by the individual trees into a single order to be taken as a model of the agent’s preference relation. We show that when learned from examples, PLP-forests have better accuracy than single PLP-trees. We also show that the choice of a voting rule does not have a major effect on the aggregated order, thus rendering the problem of selecting the “right” rule less critical. Next, for the proposed PLP-forest preference models, we develop methods to compute optimal and near-optimal outcomes, the tasks that appear difficult for some other common preference models. Lastly, we compare our models with those based on decision trees, which brings up questions for future research.

Original languageEnglish
Title of host publicationFoundations of Information and Knowledge Systems - 10th International Symposium, FoIKS 2018, Proceedings
EditorsStefan Woltran, Flavio Ferrarotti
Number of pages19
StatePublished - 2018
Event10th International Symposium on Foundations of Information and Knowledge Systems, FoIKS 2018 - Budapest, Hungary
Duration: May 14 2018May 18 2018

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume10833 LNCS
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349


Conference10th International Symposium on Foundations of Information and Knowledge Systems, FoIKS 2018

Bibliographical note

Publisher Copyright:
© Springer International Publishing AG, part of Springer Nature 2018.

ASJC Scopus subject areas

  • Theoretical Computer Science
  • Computer Science (all)

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