ARNAULD: Preference Elicitation For Interface Optimization

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. more >>

SUPPLE: Automatically Generating Adaptive User Interfaces

SUPPLE is an application and device-independent system, currently under development at University of Washington, that automatically generates user interfaces for a wide variety of display devices. SUPPLE uses decision-theoretic optimization to render an interface from an abstract functional specification and an interchangeable device model. SUPPLE can use information from the user model to automatically adapt user interfaces to different tasks and work styles while also prividing extensive customization mechanisms that allow for modifications to the appearance, organization and navigational structure of the user interface. more >>

Proteus: Personalizing Web Sites For Mobile Users

Most web sites today are designed with a "one-size-fits-all" philosophy. The web site designer "specs out" the needs of the web visitor, and then creates the content accordingly. However, one size frequently does not fit all. Visitors may use the site in ways unanticipated by the site designer. For example, a site that contains a broad array of information, such as a software support site, may be structured with as deep hierarchcy according to product, version, operating system, and error condition. However, visitors may follow only a few paths through this site, and the deep hierarchy is needlessly burdensome. In addition, a visitor's web client may be constrained in same way, perhaps by a small screen size or slow network connection. Content designed for large, color displays and fast networks is seldom useful for such constrained devices. more >>

DIAManD: Adaptive Interfaces for Machine Learning

Our previous work on the SMARTedit text-editing system led us to the same conclusion: machine learning tasks can benefit from careful management of interactions with the user. The result is a system that requires fewer examples to learn a concept, acquires domain knowledge more effectively, and is more appropriate for users unfamiliar with machine learning.

Our current work involves studying how to implement and manage novel interactions in intelligent interfaces. Although the work began with the SMARTedit system, we are currently investigating other machine learning domains which could benefit from enhanced interfaces.

Our first adaptive interface for machine learning called DIAManD sits atop the SMARTedit text-editing system. The interface exposes a small set of different interactions to the user and provides recommendations on which interaction to activate. DIAManD automatically initiates the best interaction if its confidence in that interaction is high enough. Thus, the system becomes an active participant in the discourse with the user. The user can still exercise full control (overriding the system's choice), but many users in our preliminary testing are delighted to receive feedback from the system and loath to ignore its advice. more >>

SMARTedit: Programming By Demonstration Using Version Space Algebra

Computer applications are difficult to customize for the non-programmer faced with repetitive tasks. Macro recording systems allow users to record sequences of actions and play them back, but many users don't use them. Often the time and effort required to construct and debug macros that work correctly in all cases exceeds the time and effort required to do the repetitive task by hand.

Macro systems are a form of programming by demonstration (PBD), in which users demonstrate their task to the system, which then learns a program that accomplishes the task. The benefits of PBD are that the user doesn't need to learn an arcane programming language in order to automate repetitive tasks, and she demonstrates her programs using an interface with she is already familiar.

This research aims to address the drawbacks of existing macro recorder systems by combining a simple macro recording interface with AI machine learning techniques. Our goal is to apply algorithms from AI to construct a robust, domain-independent programming by demonstration system. more >>

Adaptive Web Sites

Todays Web sites are intricate but not intelligent; while Web navigation is dynamic and idiosyncratic, all too often Web sites are fossils cast in HTML. In response, this project investigates adaptive Web sites: sites that automatically improve their organization and presentation by learning from visitor access patterns. Adaptive Web sites mine the data buried in Web server logs to produce more easily navigable Web sites. more >>