  User preferences for automated assistance often vary widely, depending on the
situation, and quality or presentation of help. Developing effectivemodels to
learn individual preferences online requires domain models that associate
observations of user behavior with their utility functions, which in turn can
be constructed using utility elicitation techniques. However, most elicitation
methods ask for users' predicted utilities based on hypothetical scenarios
rather than more realistic experienced utilities. This is especially true in
interface customization, where users are asked to assess novel interface
designs. We propose experiential utility elicitation methods for customization
and compare these to predictivemethods. As experienced utilities have been
argued to better reflect true preferences in behavioral decision making, the
purpose here is to investigate accurate and efficient procedures that are
suitable for software domains. Unlike conventional elicitation, our results
indicate that an experiential approach helps people understand stochastic
outcomes, as well as better appreciate the sequential utility of intelligent
assistance.
