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Andrew Faulring, Brad Myers, Ken Mohnkern,
Bradley Schmerl, Aaron Steinfeld, John Zimmerman, Asim Smailagic, Jeffery Hansen and Daniel Siewioreck. Agent-Assisted Task Management that Reduces Email Overhead. In Proceedings of the 14th International Conference on Intelligent User Interfaces, Hong Kong, China, ACM Press. pp. 61-70. February 7-10, 2010. Honorable Mention Award in the Best Paper
Contest.
DOI.
Abstract: RADAR is a multiagent system with a mixed-initiative user
interface designed to help office workers cope with email
overload. RADAR agents observe experts to learn models
of their strategies and then use the models to assist other
people who are working on similar tasks. The agents’ assistance helps a person to transition from the normal email-centric workflow to a more efficient task-centric workflow.
The Email Classifier learns to identify tasks contained within emails and then inspects new emails for similar tasks. A
novel task-management user interface displays the found
tasks in a to-do list, which has integrated support for performing the tasks. The Multitask Coordination Assistant
learns a model of the order in which experts perform tasks
and then suggests a schedule to other people who are working on similar tasks. A novel Progress Bar displays the suggested schedule of incomplete tasks as well as the completed tasks. A large evaluation demonstrated that novice
users confronted with an email overload test performed
significantly better (a 37% better overall score with a factor
of four fewer errors) when assisted by the RADAR agents.
David Garlan and
Bradley Schmerl. An Architecture for Personal Cognitive Assistance. In 18th International Conference on Software Engineering and Knowledge Engineering, San Francisco Bay, USA. July 5-7, 2006.
Abstract: Current desktop environments provide weak support for carrying out complex user-oriented tasks. Although individual applications are becoming increasingly sophisticated and featurerich, users must map their high-level goals to the low-level operational vocabulary of applications, and deal with a myriad of routine tasks (such as keeping up with email, keeping calendars and web sites up-to-date, etc.). An alternative vision is that of a personal cognitive assistant. Like a good secretary, such an assistant would help users accomplish their high-level goals, coordinating the use of multiple applications, automatically handling routine tasks, and, most importantly, adapting to the individual needs of a user over time. In this paper we describe the architecture and its implementation for a personal cognitive assistant called RADAR. Key features include (a) extensibility through the use of a plug-in agent architecture (b) transparent integration with legacy applications and data of today?s desktop environments, and (c) extensive use of learning so that the environment adapts to the individual user over time.