A Hybrid Approach to Reasoning with Partially Elicited Preference Models

TL;DR

This paper introduces a hybrid decision-making method combining classical utility theory with qualitative logic-based preference reasoning, enabling effective decision support with partially specified preferences.

Contribution

It presents a novel hybrid approach that integrates quantitative utility models with qualitative preference statements for improved decision making.

Findings

Effectively constrains utility functions using qualitative comparisons.

Identifies sub-optimal decision alternatives based on partial preferences.

Demonstrates synergy between quantitative and qualitative decision methods.

Abstract

Classical Decision Theory provides a normative framework for representing and reasoning about complex preferences. Straightforward application of this theory to automate decision making is difficult due to high elicitation cost. In response to this problem, researchers have recently developed a number of qualitative, logic-oriented approaches for representing and reasoning about references. While effectively addressing some expressiveness issues, these logics have not proven powerful enough for building practical automated decision making systems. In this paper we present a hybrid approach to preference elicitation and decision making that is grounded in classical multi-attribute utility theory, but can make effective use of the expressive power of qualitative approaches. Specifically, assuming a partially specified multilinear utility function, we show how comparative statements about classes of decision alternatives can be used to further constrain the utility function and thus identify sup-optimal alternatives. This work demonstrates that quantitative and qualitative approaches can be synergistically integrated to provide effective and flexible decision support.