Who's Asking for Help? A Bayesian Approach to Intelligent Assistance

Speaker: Bowen Hui (U of Toronto)

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 offer considerable benefit by adapting to both different types and changing attitudes. We then develop a more realistic user model, using behavioural data from 45 users to learn model parameters and the topology of our proposed user types. With the new model, we conducted a usability experiment with 4 users and 4 different policies. These experiments, while preliminary, show encouraging results for our adaptive policy.