Learning Importance of Preferences

14 pages•Published: September 29, 2016

Abstract

We study the problem of learning the importance of preferences in preference profiles in two important cases: when individual preferences are aggregated by the ranked Pareto rule, and when they are aggregated by positional scoring rules. For the ranked Pareto rule, we provide a polynomial-time algorithm that finds a ranking of preferences such that the ranked profile correctly decides all the examples, whenever such a ranking exists. We also show that the problem to learn a ranking maximizing the number of correctly decided examples (also under the ranked Pareto rule) is NP-hard. We obtain similar results for the case of weighted profiles when positional scoring rules are used for aggregation.

Keyphrases: pareto rule, positional scoring rules, preference aggregation, preference importance, preference reasoning

In: Christoph Benzmüller, Geoff Sutcliffe and Raul Rojas (editors). GCAI 2016. 2nd Global Conference on Artificial Intelligence, vol 41, pages 81-94.

Links:	https://easychair.org/publications/paper/NMJm
	https://doi.org/10.29007/v68w

BibTeX entry

@inproceedings{GCAI2016:Learning_Importance_Preferences,
  author    = {Ying Zhu and Mirek Truszczynski},
  title     = {Learning Importance of Preferences},
  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/NMJm},
  doi       = {10.29007/v68w},
  pages     = {81-94},
  year      = {2016}}

Download PDF Open PDF in browser