Mixed initiative interfaces for learning tasks

Publisher Website
Google Scholar
Abstract
Applications of machine learning can be viewed as teacher-student interactions in which the teacher provides training examples and the student learns a generalization of the training examples. One such application of great interest to the IUI community is adaptive user interfaces. In the traditional learning interface, the scope of teacher-student interactions consists solely of the teacher/user providing some number of training examples to the student/learner and testing the learned model on new examples. Active learning approaches go one step beyond the traditional interaction model and allow the student to propose new training examples that are then solved by the teacher. In this paper, we propose that interfaces for machine learning should even more closely resemble human teacher-student relationships. A teacher's time and attention are precious resources. An intelligent student must proactively contribute to the learning process, by reasoning about the quality of its knowledge, collaborating with the teacher, and suggesting new examples for her to solve. The paper describes a variety of rich interaction modes that enhance the learning process and presents a decision-theoretic framework, called DIAManD, for choosing the best interaction. We apply the framework to the SMARTedit programming by demonstration system and describe experimental validation and preliminary user feedback.

This publication has 4 references indexed in Scilit: