CP-nets: A Tool for Representing and Reasoning withConditional Ceteris Paribus Preference Statements

TL;DR

This paper introduces CP-nets, a qualitative graphical model for representing and reasoning with conditional preferences, enabling efficient decision-making based on user preferences expressed in a natural, compact form.

Contribution

The paper presents a formal semantics for CP-nets and demonstrates how their structure can be used for various inference tasks in preference reasoning.

Findings

CP-nets effectively model conditional preferences.

The structure allows efficient dominance and ordering inference.

CP-nets facilitate decision-making with qualitative preference data.

Abstract

Information about user preferences plays a key role in automated decision making. In many domains it is desirable to assess such preferences in a qualitative rather than quantitative way. In this paper, we propose a qualitative graphical representation of preferences that reflects conditional dependence and independence of preference statements under a ceteris paribus (all else being equal) interpretation. Such a representation is often compact and arguably quite natural in many circumstances. We provide a formal semantics for this model, and describe how the structure of the network can be exploited in several inference tasks, such as determining whether one outcome dominates (is preferred to) another, ordering a set outcomes according to the preference relation, and constructing the best outcome subject to available evidence.