The Role of Model Selection in Preference Learning

Michael Huelsman, Mirosław Truszczyński

Research output: Contribution to journalConference articlepeer-review

Abstract

Learning preferences of an agent requires choosing which preference representation to use. This formalism should be expressive enough to capture a significant part of the agent’s preferences. Selecting the right formalism is generally not easy, as we have limited access to the way the agent makes her choices. It is then important to understand how “universal” particular preference representation formalisms are, that is, whether they can perform well in learning preferences of agents with a broad spectrum of preference orders. In this paper, we consider several preference representation formalisms from this perspective: lexicographic preference models, preference formulas, sets of (ranked) preference formulas, and neural networks. We find that the latter two show a good potential as general preference representation formalisms. We show that this holds true when learning preferences of a single agent but also when learning models to represent consensus preferences of a group of agents.

Original languageEnglish
JournalProceedings of the International Florida Artificial Intelligence Research Society Conference, FLAIRS
Volume34
DOIs
StatePublished - 2021
Event34th International Florida Artificial Intelligence Research Society Conference, FLAIRS-34 2021 - North Miami Beach, United States
Duration: May 16 2021May 19 2021

Bibliographical note

Funding Information:
This work was partially funded by the NSF under the grant number IIS-1618783.

Publisher Copyright:
© 2021by the authors. All rights reserved.

ASJC Scopus subject areas

  • Artificial Intelligence
  • Software

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