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Decision theory has become widely accepted in the AI community as a usefulframework for planning and decision making. Applying the framework typicallyrequires elicitation of some form of probability and utility information. Whilemuch work in AI has focused on providing representations and tools forelicitation of probabilities, relatively little work has addressed theelicitation of utility models. This imbalance is not particularly justifiedconsidering that probability models are relatively stable across probleminstances, while utility models may be different for each instance. Spendinglarge amounts of time on elicitation can be undesirable for interactive systemsused in low-stakes decision making and in time-critical decision making. Inthis paper we investigate the issues of reasoning with incomplete utilitymodels. We identify patterns of problem instances where plans can be proved tobe suboptimal if the (unknown) utility function satisfies certain conditions.We present an approach to planning and decision making that performs theutility elicitation incrementally and in a way that is informed by the domainmodel.

Problem-Focused Incremental Elicitation of Multi-Attribute Utility Models