A Hybrid Approach to Reasoning with Partially Elicited Preference Models
This work addresses the problem of high elicitation costs in automated decision-making systems, offering a flexible solution that is incremental in combining existing approaches.
The paper tackles the challenge of automating decision-making with partially specified preferences by integrating quantitative multi-attribute utility theory with qualitative logic-based approaches, showing that this hybrid method can identify sub-optimal alternatives effectively.
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.