Probabilistic Conditional Preference Networks

TL;DR

Probabilistic CP-nets (PCP-nets) are introduced as a compact way to model probabilistic preferences and noisy data, with efficient algorithms for key reasoning tasks and a surprising linear-time dominance check.

Contribution

The paper introduces PCP-nets for probabilistic preference modeling and provides efficient algorithms for core reasoning problems, including a linear-time dominance check in tree-structured CP-nets.

Findings

Efficient algorithms for probability computations in PCP-nets

Linear-time dominance checking in tree-structured CP-nets

Effective modeling of noisy and aggregated preferences

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.