UCP-Networks: A Directed Graphical Representation of Conditional Utilities
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Abstract (arXiv)
We propose a new directed graphical representation of utility functions, called UCP-networks, that combines aspects of two existing graphical 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 of preference statements - in the underlying (qualitative) preference ordering - under a {em ceteris paribus} (all else being equal) interpretation. This representation is arguably natural in many settings. Furthermore, the strong CP-semantics ensures that computation of 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. This procedure allows the network to be refined until the regret of the decision with minimax regret (with respect to the incompletely specified utility function) falls below a specified threshold (e.g., the cost of further questioning.
Key facts. Cited by 2 papers in the map (data through 14 Oct 2026). First posted on arXiv 10 Jan 2013.
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Citing papers per year: 2021: 1; 2022: 0; 2023: 0; 2024: 0; 2025: 0; 2026: 1.
2 papers.
- 2021A CP-Net based Qualitative Composition Approach for an IaaS Providercited by 1
- 2026A Causal Markov Condition for Valuecited by 0
- 2026A Causal Markov Condition for Value18 Jul 2026
- 2021A CP-Net based Qualitative Composition Approach for an IaaS Provider24 Feb 2021