Learning voting trees

Ariel D. Procaccia*, Aviv Zohar, Yoni Peleg, Jeffrey S. Rosenschein

*Corresponding author for this work

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

8 Scopus citations

Abstract

Binary voting trees provide a succinct representation for a large and prominent class of voting rules. In this paper, we investigate the PAC-learnability of this class of rules. We show that, while in general a learning algorithm would require an exponential number of samples, if the number of leaves is polynomial in the size of the set of alternatives then a polynomial training set suffices. We apply these results in an emerging theory: automated design of voting rules by learning.

Original languageAmerican English
Title of host publicationAAAI-07/IAAI-07 Proceedings
Subtitle of host publication22nd AAAI Conference on Artificial Intelligence and the 19th Innovative Applications of Artificial Intelligence Conference
Pages110-115
Number of pages6
StatePublished - 2007
EventAAAI-07/IAAI-07 Proceedings: 22nd AAAI Conference on Artificial Intelligence and the 19th Innovative Applications of Artificial Intelligence Conference - Vancouver, BC, Canada
Duration: 22 Jul 200726 Jul 2007

Publication series

NameProceedings of the National Conference on Artificial Intelligence
Volume1

Conference

ConferenceAAAI-07/IAAI-07 Proceedings: 22nd AAAI Conference on Artificial Intelligence and the 19th Innovative Applications of Artificial Intelligence Conference
Country/TerritoryCanada
CityVancouver, BC
Period22/07/0726/07/07

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