Arrow Research search
Back to UAI

UAI 2004

Compact Value-Function Representations for Qualitative Preferences

Conference Paper Accepted Paper Artificial Intelligence · Machine Learning · Uncertainty in Artificial Intelligence

Abstract

We consider the challenge of preference elicitation in systems that help users discover the most desirable item(s) within a given database. Past work on preference elicitation focused on structured models that provide a factored representation of users' preferences. Such models require less information to construct and support efficient reasoning algorithms. This paper makes two substantial contributions to this area: (1) Strong representation theorems for factored value functions. (2) A methodology that utilizes our representation results to address the problem of optimal item selection.

Authors

Keywords

No keywords are indexed for this paper.

Context

Venue
Conference on Uncertainty in Artificial Intelligence
Archive span
1985-2025
Indexed papers
3717
Paper id
367309938602289879
v2026.09.13