## Abstract

We propose a directed graphical representation of utility functions, called UCP-networks, that combines aspects of two existing preference models: generalized additive models and CP-networks. The network decomposes a utility function into a number of additive factors, with the directionality of the arcs reflecting conditional dependence in the underlying (qualitative)

preference ordering under a ceteris paribus interpretation. The CP-semantics ensures that computing optimization and dominance queries is very efficient. We also demonstrate the value of this representation in decision making. Finally, we describe an interactive elicitation procedure that takes advantage of the linear nature of the constraints on "tradeoff weights" imposed by a UCP-network.

preference ordering under a ceteris paribus interpretation. The CP-semantics ensures that computing optimization and dominance queries is very efficient. We also demonstrate the value of this representation in decision making. Finally, we describe an interactive elicitation procedure that takes advantage of the linear nature of the constraints on "tradeoff weights" imposed by a UCP-network.

Original language | English GB |
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Title of host publication | UAI '01: Proceedings of the 17th Conference in Uncertainty in Artificial Intelligence, University of Washington, Seattle, Washington, USA, August 2-5, 2001 |

Editors | Jack S. Breese, Daphne Koller |

Publisher | Morgan Kaufmann Publishers, Inc. |

Pages | 56-64 |

Number of pages | 9 |

State | Published - 2001 |