The design and evaluation of user interfaces for the RADAR learning personal assistant

Andrew Faulring, Ken Mohnkern, Aaron Steinfeld and Brad A. Myers
Journal Article, Carnegie Mellon University, AI Magazine, Vol. 30, No. 4, pp. 74-84, January, 2009

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The RADAR project developed a large multi-agent system with a mixed-initiative user interface designed to help office workers cope with e-mail overload. Most RADAR agents observe experts performing tasks and then assist other users who are performing similar tasks. The interaction design for RADAR focused on developing user interfaces that allowed the intelligent functionality to improve the user’s workflow without frustrating the user when the system’s suggestions were either unhelpful or simply incorrect. For example, with regard to autonomy, the RADAR agents were allowed much flexibility in selecting ways to assist the user but were restricted from taking actions that would be visible to other people. This policy ensured that the user remained in control and mitigated the negative effects of mistakes. A large evaluation of RADAR demonstrated that novice users confronted with an e-mail overload test performed significantly better, achieving a 37 percent better overall score when assisted by RADAR. The evaluation showed that AI technologies can help users accomplish their goals.

author = {Andrew Faulring and Ken Mohnkern and Aaron Steinfeld and Brad A. Myers},
title = {The design and evaluation of user interfaces for the RADAR learning personal assistant},
journal = {AI Magazine},
year = {2009},
month = {January},
volume = {30},
number = {4},
pages = {74-84},
} 2017-09-13T10:41:20-04:00