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Article

Vio: a mixed-initiative approach to learning and automating procedural update tasks

Authors:

John Zimmerman,

Anthony Tomasic,

Jason Cornwell,

Robert Martin McGuireAuthors Info & Claims

CHI '07: Proceedings of the SIGCHI Conference on Human Factors in Computing Systems

Pages 1445 - 1454

https://doi.org/10.1145/1240624.1240843

Published: 29 April 2007 Publication History

Abstract

Today many workers spend too much of their time translating their co-workers' requests into structures that information systems can understand. This paper presents the novel interaction design and evaluation of VIO, an agent that helps workers trans late request. VIO monitors requests and makes suggestions to speed up the translation. VIO allows users to quickly correct agent errors. These corrections are used to improve agent performance as it learns to automate work. Our evaluations demonstrate that this type of agent can significantly reduce task completion time, freeing workers from mundane tasks.

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References

[1]

Belotti, V., Ducheneaut, M., Howard, M., Smith, I. Taking Email to Task: The Design and Evaluation of a Task Management Centered E-Mail Tool. In Proc. of CHI, ACM Press (2003), 345--352.

Digital Library

[2]

Van Schaik, P., Jing, J. Five Psychometric Scales for Online Measurement of the Quality of Human-Computer Interaction in Web Sites. International Journal of Human-Computer Interaction 18, 3 (2005), 309--322.

[3]

Cypher, A., Eager: Programming Repetitive Tasks by Demonstration. in Watch What I Do: Programming By Demonsration Cypher, A. et. al. (eds), MIT Press, 1993.

Digital Library

[4]

Ducheneaut, N., Bellotti, V. E-mail as habitat: An exploration of embedded personal information management. Interactions 8, 5 (2001), 30--38.

Digital Library

[5]

Ferguson, G., Allen, J., Miller, B. TRAINS-95: Towards a Mixed-Initiative Planning Assistant. In Proc. of AIPS, AAAI Press (1996) 70--77.

[6]

Fogarty, J., Hudson, S.E., Atkeson, G.G., Avrahami, D., Forlizzi, J., Kiesler, S., Lee, J.C., Yang, J. Predicting human interruptibility with sensors. ACM Transactions on Computer-Human Interaction 12, 1 (2004), 119--146.

Digital Library

[7]

Hearst, M. Mixed Initiative Interaction. IEEE Intelligent Systems 14, 5 (1999), 14--23.

Digital Library

[8]

Horvitz, E. Principles of Mixed Initiative User Interfaces. Proc. of CHI, ACM Press (1999), 159--166.

Digital Library

[9]

John, B.E. Information processing and skilled behavior. In J. M. Carroll (Ed.) Toward a multidisciplinary science of human computer interaction Morgan Kaufman (2003), 55--101.

[10]

John, B.E., Salvucci. D.D., Multipurpose prototypes for assessing user interfaces in pervasive computing systems. IEEE Pervasive Computing 4, 4 (2005), 27--34.

Digital Library

[11]

Lieberman, H. Autonomous interface agents. Proc. of CHI, ACM Press (1997), 67--74.

Digital Library

[12]

Liebermann, H. Beating Some Common Sense into Interactive Applications. Seminar talk given at Carnegie Mellon's Human-Computer Interaction Institute. (February 23, 2005).

[13]

Lockerd, A., Pham, H., Sharon, T., Selker, T. Mr. Web: An Automated Interactive Webmaster. Ext. Abstracts CHI, ACM Press (2003), 812--813.

Digital Library

[14]

Malone, T., Grant, K., Lai, K., Rao, R., Rosenblitt, D. Semistructured Messages are Surprisingly Useful for Computer-Supported Coordination. ACM Transactions on Information Systems 5, 2 (1987), 115--131.

Digital Library

[15]

Meng, F. A Natural Language Interface for Information Retrieval from Forms on the World Wide Web. Proc. of IS, ACM Press (1999), 540--545.

Digital Library

[16]

Remedy: http://www.bmc.com/remedy/

[17]

Shneiderman, B., Maes, P. Direct Manipulation Versus Interface Agents. Interactions 4, 6, ACM Press (1997), 43--61.

Digital Library

[18]

Stylos, J, Myers, B.A., Faulring, A. Citrine: Providing Intelligent Copy-and-Paste. Proc. of UIST, ACM Press (2004), 185--188.

Digital Library

[19]

Tomasic, A., Simmons, I., Zimmerman, J., Experimental Evaluation of Processing Information Intent via Weak Labeling. Technical Report, Department of Computer Science, Carnegie Mellon University, pending.

Cited By

Sun LLi ZZhou ZLou SLi WZhang Y(2023)Towards the conceptual design of ML-enhanced products: the UX value framework and the CoMLUX design processArtificial Intelligence for Engineering Design, Analysis and Manufacturing10.1017/S089006042200027037Online publication date: 30-Mar-2023
https://doi.org/10.1017/S0890060422000270
Lam KGutwin CKlarkowski MCockburn A(2022)More Errors vs. Longer Commands: The Effects of Repetition and Reduced Expressiveness on Input Interpretation Error, Learning, and EffortProceedings of the 2022 CHI Conference on Human Factors in Computing Systems10.1145/3491102.3502079(1-17)Online publication date: 29-Apr-2022
https://dl.acm.org/doi/10.1145/3491102.3502079
Lam KGutwin CKlarkowski MCockburn AKitamura YQuigley AIsbister KIgarashi TBjørn PDrucker S(2021)The Effects of System Interpretation Errors on Learning New Input MechanismsProceedings of the 2021 CHI Conference on Human Factors in Computing Systems10.1145/3411764.3445366(1-13)Online publication date: 6-May-2021
https://dl.acm.org/doi/10.1145/3411764.3445366
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Index Terms

Vio: a mixed-initiative approach to learning and automating procedural update tasks
1. Computing methodologies
  1. Artificial intelligence
    1. Natural language processing
2. Human-centered computing
  1. Human computer interaction (HCI)
    1. Interaction paradigms

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Published In

cover image ACM Conferences

CHI '07: Proceedings of the SIGCHI Conference on Human Factors in Computing Systems

April 2007

1654 pages

ISBN:9781595935939

DOI:10.1145/1240624

General Chair:
Mary Beth Rosson
The Pennsylvania State University, USA
,
Program Chair:
David Gilmore
Intel, USA

Copyright © 2007 ACM.

Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

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Published: 29 April 2007

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CHI07

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CHI07: CHI Conference on Human Factors in Computing Systems

April 28 - May 3, 2007

California, San Jose, USA

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CHI '07 Paper Acceptance Rate 182 of 840 submissions, 22%;

Overall Acceptance Rate 6,199 of 26,314 submissions, 24%

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Cited By

Sun LLi ZZhou ZLou SLi WZhang Y(2023)Towards the conceptual design of ML-enhanced products: the UX value framework and the CoMLUX design processArtificial Intelligence for Engineering Design, Analysis and Manufacturing10.1017/S089006042200027037Online publication date: 30-Mar-2023
https://doi.org/10.1017/S0890060422000270
Lam KGutwin CKlarkowski MCockburn A(2022)More Errors vs. Longer Commands: The Effects of Repetition and Reduced Expressiveness on Input Interpretation Error, Learning, and EffortProceedings of the 2022 CHI Conference on Human Factors in Computing Systems10.1145/3491102.3502079(1-17)Online publication date: 29-Apr-2022
https://dl.acm.org/doi/10.1145/3491102.3502079
Lam KGutwin CKlarkowski MCockburn AKitamura YQuigley AIsbister KIgarashi TBjørn PDrucker S(2021)The Effects of System Interpretation Errors on Learning New Input MechanismsProceedings of the 2021 CHI Conference on Human Factors in Computing Systems10.1145/3411764.3445366(1-13)Online publication date: 6-May-2021
https://dl.acm.org/doi/10.1145/3411764.3445366
Romero OHaig AKirabo LYang QZimmerman JTomasic ASteinfeld A(2020)A Long-Term Evaluation of Adaptive Interface Design for Mobile Transit Information22nd International Conference on Human-Computer Interaction with Mobile Devices and Services10.1145/3379503.3403536(1-11)Online publication date: 5-Oct-2020
https://dl.acm.org/doi/10.1145/3379503.3403536
Yang QSteinfeld ARosé CZimmerman JBernhaupt RMueller FVerweij DAndres JMcGrenere JCockburn AAvellino IGoguey ABjørn PZhao SSamson BKocielnik R(2020)Re-examining Whether, Why, and How Human-AI Interaction Is Uniquely Difficult to DesignProceedings of the 2020 CHI Conference on Human Factors in Computing Systems10.1145/3313831.3376301(1-13)Online publication date: 21-Apr-2020
https://dl.acm.org/doi/10.1145/3313831.3376301
Dove GFayard ABernhaupt RMueller FVerweij DAndres JMcGrenere JCockburn AAvellino IGoguey ABjørn PZhao SSamson BKocielnik R(2020)Monsters, Metaphors, and Machine LearningProceedings of the 2020 CHI Conference on Human Factors in Computing Systems10.1145/3313831.3376275(1-17)Online publication date: 21-Apr-2020
https://dl.acm.org/doi/10.1145/3313831.3376275
Kim DLim YBrewster SFitzpatrick GCox AKostakos V(2019)Co-Performing AgentProceedings of the 2019 CHI Conference on Human Factors in Computing Systems10.1145/3290605.3300714(1-14)Online publication date: 2-May-2019
https://dl.acm.org/doi/10.1145/3290605.3300714
Schnabel TBennett PJoachims TCulpepper JMoffat ABennett PLerman K(2019)Shaping Feedback Data in Recommender Systems with Interventions Based on Information Foraging TheoryProceedings of the Twelfth ACM International Conference on Web Search and Data Mining10.1145/3289600.3290974(546-554)Online publication date: 30-Jan-2019
https://dl.acm.org/doi/10.1145/3289600.3290974
Dove GHalskov KForlizzi JZimmerman JMark GFussell SLampe Cschraefel mHourcade JAppert CWigdor D(2017)UX Design InnovationProceedings of the 2017 CHI Conference on Human Factors in Computing Systems10.1145/3025453.3025739(278-288)Online publication date: 2-May-2017
https://dl.acm.org/doi/10.1145/3025453.3025739
Yang QZimmerman JSteinfeld ATomasic AFoth MJu WSchroeter RViller S(2016)Planning Adaptive Mobile Experiences When WireframingProceedings of the 2016 ACM Conference on Designing Interactive Systems10.1145/2901790.2901858(565-576)Online publication date: 4-Jun-2016
https://dl.acm.org/doi/10.1145/2901790.2901858
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