As part of the Distinguished Lecture Series, Dr. Karen Myers is presenting a talk at the Bahen Centre this Tuesday, November 18th. The lecture is hosted in BA1170 at 11:00 am.
Learning from Demonstration Technology: A Tale of Two Applications
Abstract:
Learning from demonstration technology has seen increased focus in recent years as a means to endow computers with capabilities that might otherwise be difficult or time-consuming for a user to program. This talk describes two efforts that employ learning from demonstration technology to quite distinct ends. The first is to provide a capability that supports users with no programming experience in the creation of procedures that automate repetitive or time-consuming tasks. This capability has been operationally deployed within a collaborative planning environment that is used widely by the U.S. Army. The second is to support automated performance evaluation of students as they seek to acquire complex procedural skills through training in virtual environments. In this second case, instructional content developers employ learning from demonstration technology to create solution models for training exercises. An automated assessment capability employs soft graph matching to align a trace of a students response to an exercise with the solution models for that exercise, providing a flexible basis for evaluating student performance. In contrast to intelligent tutoring systems that force students to follow a pre-specified solution trajectory, our approach enables meaningful feedback in domains where solutions can have significant variability.
 |
Karen Myers is a Principal Scientist within the Artificial Intelligence Center at SRI International, where she leads a team focused on developing intelligent systems that facilitate man-machine collaboration. Myers has led the development of several AI technologies that have been successfully transitioned into operational use in areas that span collaborative systems, task management, and learning from demonstration. Her research interests include autonomy, multi-agent systems, automated planning, personalization, and mixed-initiative problem solving |