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Learning Partial Lexicographic Preference Trees and Forests over Multi-Valued Attributes

15 pagesPublished: September 29, 2016

Abstract

\tit{Partial lexicographic preference trees}, or \tit{PLP-trees}, form an intuitive formalism for compact representation of qualitative preferences over combinatorial domains. We show that PLP-trees can be used to accurately model preferences arising in practical situations, and that high-accuracy PLP-trees can be effectively learned. We also propose and study learning methods for a variant of our model based on the concept of a PLP-forest, a collection of PLP-trees, where the preference order specified by a PLP-forest is obtained by aggregating the orders of its constituent PLP-trees. Our results demonstrate the potential of both approaches, with learning PLP-forests showing particularly promising behavior.

Keyphrases: learning preference models, partial lexicographic preference forests, partial lexicographic preference trees, preference reasoning, preference representation

In: Christoph Benzmüller, Geoff Sutcliffe and Raul Rojas (editors). GCAI 2016. 2nd Global Conference on Artificial Intelligence, vol 41, pages 314-328.

BibTeX entry
@inproceedings{GCAI2016:Learning_Partial_Lexicographic_Preference,
  author    = {Xudong Liu and Mirek Truszczynski},
  title     = {Learning Partial Lexicographic Preference Trees and Forests over Multi-Valued Attributes},
  booktitle = {GCAI 2016. 2nd Global Conference on Artificial Intelligence},
  editor    = {Christoph Benzmüller and Geoff Sutcliffe and Raul Rojas},
  series    = {EPiC Series in Computing},
  volume    = {41},
  publisher = {EasyChair},
  bibsource = {EasyChair, https://easychair.org},
  issn      = {2398-7340},
  url       = {/publications/paper/6z},
  doi       = {10.29007/xtl4},
  pages     = {314-328},
  year      = {2016}}
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