Title

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

Document Type

Conference Proceeding

Publication Date

1-1-2018

Subject Area

ARRAY(0x5596d5e1e880)

Abstract

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.

Publication Title

Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)

Volume

10833 LNCS

First Page

284

Last Page

302

Digital Object Identifier (DOI)

10.1007/978-3-319-90050-6_16

ISSN

03029743

E-ISSN

16113349

ISBN

9783319900490

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