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- W2772071247 abstract "A Probabilistic Conditional Preference network (PCP-net) provides a compact representation of preferences characterized with uncertainty. We propose to enrich the expressive power of the PCP-net by adding constraints between some of the variables. We call this new model, the Constrained PCP-net (CPCP-net). We study the key preference reasoning task with the proposed CPCP-net which consists in finding the most probable optimal outcome i.e. the most probable outcome that best represents the preferences while satisfying all the constraints. In this regard, a variant of the Branch and Bound algorithm has been proposed and experimentally evaluated on CPCP-net instances, randomly generated based on the RB-model. The results of these experiments show that the new proposed solving method is capable of returning the most probable optimal outcome in a reasonable time." @default.
- W2772071247 created "2017-12-22" @default.
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- W2772071247 date "2017-10-01" @default.
- W2772071247 modified "2023-09-27" @default.
- W2772071247 title "Representing and reasoning with constrained PCP-nets" @default.
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- W2772071247 doi "https://doi.org/10.1109/smc.2017.8122742" @default.
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