Probabilistic Conditional Preference Networks

Damien Bigot, Bruno Zanuttini, Helene Fargier, Jérôme Mengin
Proceedings of the 29th Conference on Uncertainty in Artificial Intelligence, PMLR R11:302-311, 2013.

Abstract

This paper proposes a “probabilistic” exten- sion of conditional preference networks as a way to compactly represent a probability dis- tributions over preference orderings. It stud- ies the probabilistic counterparts of the main reasoning tasks, namely dominance testing and optimisation from the algorithmical and complexity viewpoints. Efficient algorithms for tree-structured probabilistic CP-nets are given. As a by-product we obtain a linear- time algorithm for dominance testing in stan- dard, tree-structured CP-nets.

Cite this Paper

BibTeX
@InProceedings{pmlr-vR11-bigot13a, title = {Probabilistic Conditional Preference Networks}, author = {Bigot, Damien and Zanuttini, Bruno and Fargier, Helene and Mengin, J{\'e}r{\^o}me}, booktitle = {Proceedings of the 29th Conference on Uncertainty in Artificial Intelligence}, pages = {302--311}, year = {2013}, editor = {Nicholson, Ann and Smyth, Padhraic}, volume = {R11}, series = {Proceedings of Machine Learning Research}, month = {12--14 Jul}, publisher = {PMLR}, pdf = {https://raw.githubusercontent.com/mlresearch/r11/main/assets/bigot13a/bigot13a.pdf}, url = {https://proceedings.mlr.press/r11/bigot13a.html}, abstract = {This paper proposes a “probabilistic” exten- sion of conditional preference networks as a way to compactly represent a probability dis- tributions over preference orderings. It stud- ies the probabilistic counterparts of the main reasoning tasks, namely dominance testing and optimisation from the algorithmical and complexity viewpoints. Efficient algorithms for tree-structured probabilistic CP-nets are given. As a by-product we obtain a linear- time algorithm for dominance testing in stan- dard, tree-structured CP-nets.}, note = {Reissued by PMLR on 04 October 2026.} }
Endnote
%0 Conference Paper %T Probabilistic Conditional Preference Networks %A Damien Bigot %A Bruno Zanuttini %A Helene Fargier %A Jérôme Mengin %B Proceedings of the 29th Conference on Uncertainty in Artificial Intelligence %C Proceedings of Machine Learning Research %D 2013 %E Ann Nicholson %E Padhraic Smyth %F pmlr-vR11-bigot13a %I PMLR %P 302--311 %U https://proceedings.mlr.press/r11/bigot13a.html %V R11 %X This paper proposes a “probabilistic” exten- sion of conditional preference networks as a way to compactly represent a probability dis- tributions over preference orderings. It stud- ies the probabilistic counterparts of the main reasoning tasks, namely dominance testing and optimisation from the algorithmical and complexity viewpoints. Efficient algorithms for tree-structured probabilistic CP-nets are given. As a by-product we obtain a linear- time algorithm for dominance testing in stan- dard, tree-structured CP-nets. %Z Reissued by PMLR on 04 October 2026.
APA
Bigot, D., Zanuttini, B., Fargier, H. & Mengin, J.. (2013). Probabilistic Conditional Preference Networks. Proceedings of the 29th Conference on Uncertainty in Artificial Intelligence, in Proceedings of Machine Learning Research R11:302-311 Available from https://proceedings.mlr.press/r11/bigot13a.html. Reissued by PMLR on 04 October 2026.

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