Uncertainty in Artificial Intelligence
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Probabilistic Conditional Preference Networks
Damien Bigot, Bruno Zanuttini, Helene Fargier, Jerome Mengin
Abstract:
In order to represent the preferences of a group of individuals, we introduce Probabilistic CP-nets (PCP-nets). PCP-nets provide a compact language for representing probability distributions over preference orderings. We argue that they are useful for aggregating preferences or modelling noisy preferences. Then we give efficient algorithms for the main reasoning problems, namely for computing the probability that a given outcome is preferred to another one, and the probability that a given outcome is optimal. As a by-product, we obtain an unexpected linear-time algorithm for checking dominance in a standard, tree-structured CP-net.
Keywords:
Pages: 72-81
PS Link:
PDF Link: /papers/13/p72-bigot.pdf
BibTex:
@INPROCEEDINGS{Bigot13,
AUTHOR = "Damien Bigot and Bruno Zanuttini and Helene Fargier and Jerome Mengin",
TITLE = "Probabilistic Conditional Preference Networks",
BOOKTITLE = "Proceedings of the Twenty-Ninth Conference Annual Conference on Uncertainty in Artificial Intelligence (UAI-13)",
PUBLISHER = "AUAI Press",
ADDRESS = "Corvallis, Oregon",
YEAR = "2013",
PAGES = "72--81"
}


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