Learning conditional preference networks

11 years 5 months ago
Learning conditional preference networks
We investigate the problem of eliciting CP-nets in the well-known model of exact learning with equivalence and membership queries. The goal is to identify a preference ordering with a binary-valued CP-net by guiding the user through a sequence of queries. Each example is a dominance test on some pair of outcomes. In this setting, we show that acyclic CP-nets are not learnable with equivalence queries alone, while they are learnable with the help of membership queries if the supplied examples are restricted to swaps. A similar property holds for tree CP-nets with arbitrary examples. In fact, membership queries allow us to provide attributeefficient algorithms for which the query complexity is only logarithmic in the number of attributes. Such results highlight the utility of this model for eliciting CP-nets in large multi-attribute domains.
Frédéric Koriche, Bruno Zanuttini
Added 08 Dec 2010
Updated 08 Dec 2010
Type Journal
Year 2010
Where AI
Authors Frédéric Koriche, Bruno Zanuttini
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