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

TLDR

User preferences are crucial for automated decision making, and many domains prefer qualitative over quantitative assessment. The paper proposes a qualitative graphical representation of preferences that captures conditional dependence and independence under a ceteris‑paribus interpretation. The authors formalize the model and exploit its network structure to perform inference tasks such as dominance testing, outcome ranking, and best‑outcome construction given evidence. The representation is compact and natural, enabling efficient inference for dominance, ranking, and best‑outcome construction.

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.