Preference learning and optimization for partial lexicographic preference forests over combinatorial domains
Document Type
Conference Proceeding
Publication Date
1-1-2018
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
Citation Information
Liu, & Truszczynski, M. (2018). Preference Learning and Optimization for Partial Lexicographic Preference Forests over Combinatorial Domains. In Foundations of Information and Knowledge Systems (pp. 284–302). Springer International Publishing. https://doi.org/10.1007/978-3-319-90050-6_16