Bowen Hui
Department of Computer Science
University of Toronto
Toronto, ON M5S 3H5
email: bowen@cs.toronto.edu
Craig Boutilier
Department of Computer Science
University of Toronto
Toronto, ON M5S 3H5
email: cebly@cs.toronto.edu
Abstract
Automated software customization is drawing increasing attention
as a means to help users deal with the scope, complexity, potential
intrusiveness, and ever-changing nature of modern software. The
ability to automatically customize functionality, interfaces, and advice
to specific users is made more difficult by the uncertainty about
the needs of specific individuals and their preferences for interaction.
Following recent probabilistic techniques in user modeling,
we model our user with a dynamic Bayesian network (DBN) and
propose to explicitly infer the "user's type"--a composite of personality
and affect variables--in real time. We design the system
to reason about the impact of its actions given the user's current
attitudes. To illustrate the benefits of this approach, we describe a
DBN model for a text-editing help task. We show, through simulations,
that user types can be inferred quickly, and that a myopic
policy offers considerable benefit by adapting to both different
types and changing attitudes. We then develop a more realistic
user model, using behavioral data from 45 users to learn model parameters
and the topology of our proposed user types. With the
new model, we conduct a usability experiment with 4 users and
4 different policies. These experiments, while preliminary, show
encouraging results for our adaptive policy.
To appear, IUI-2006
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