ProblemFocused Incremental Elicitation of MultiAttribute Utility Models
Vu Ha and Peter Haddawy Decision Systems and Artificial Intelligence Lab Dept. of EE&CS University of WisconsinMilwaukee Milwaukee, WI 53201
Decision theory has become widely accepted in the AI community as a useful framework for planning and decision making. Applying the framework typically requires elicitation of some form of probability and utility in formation. While much work in AI has fo cused on providing representations and tools for elicitation of probabilities, relatively little work has addressed the elicitation of utility models. This imbalance is not particularly justified considering that probability models are relatively stable across problem instances, while utility models may be different for each instance. Spending large amounts of time on elicitation can be undesirable for interactive systems used in lowstakes decision making and in timecritical decision making. In this paper we investigate the issues of reasoning with incomplete utility models. We identify patterns of problem instances where plans can be proved to be suboptimal if the (un known) utility function satisfies certain con ditions. We present an approach to planning and decision making that performs the utility elicitation incrementally and in a way that is informed by the domain model.