Overview

Recent years have revealed a trend towards increasing use of optimization as a method for automatically designing aspects of an interface's interaction with the user. In most cases, this optimization may be thought of as decision-theoretic -- the objective is to minimize the expected cost of a user's interactions or (equivalently) to maximize the user's expected utility. While decision-theoretic optimization provides a powerful, flexible, and principled approach for these systems, the quality of the resulting solution is completely dependent on the accuracy of the underlying utility or cost function. Unfortunately, determining the correct utility function is a complex, time-consuming, and error-prone task. While domainspecific learning techniques have been used occasionally, most practitioners parameterize the utility function and then engage in a laborious and unreliable process of hand-tuning.

Our work on Arnauld focuses on developing the interaction techniques and algorithms necessary to easily and robustly elicit the correct sets of parameters from the original developers or from the end users.

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