ARNAULD: Preference Elicitation For Interface Optimization

ARNAULD Project 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 domain-specific learning techniques have been used occasionally, most practitioners parameterize the utility function and then engage in a laborious and unreliable process of hand-tuning.

Krzysztof Z. Gajos, Jacob O. Wobbrock, and Daniel S. Weld. Improving the performance of motor-impaired users with automatically-generated, ability-based interfaces. In CHI '08: Proceeding of the twenty-sixth annual SIGCHI conference on Human factors in computing systems, pages 1257-1266, New York, NY, USA, 2008. ACM.   Best Paper Award  
[Abstract, BibTeX, Video, Authorizer, etc.]

Krzysztof Gajos and Daniel S. Weld. Preference elicitation for interface optimization. In UIST '05: Proceedings of the 18th annual ACM symposium on User interface software and technology, pages 173-182, New York, NY, USA, 2005. ACM Press.
[Abstract, BibTeX, Slides, Authorizer, etc.]