research-article

A probabilistic mental model for estimating disruption

Authors:

Grant Partridge,

Craig BoutilierAuthors Info & Claims

IUI '09: Proceedings of the 14th international conference on Intelligent user interfaces

Pages 287 - 296

https://doi.org/10.1145/1502650.1502691

Published: 08 February 2009 Publication History

Abstract

Adaptive software systems are intended to modify their appearance, performance or functionality to the needs and preferences of different users. A key bottleneck in building effective adaptive systems is accounting for the cost of disruption to a user's mental model of the application caused by the system's adaptive behaviour. In this work, we propose a probabilistic approach to modeling the cost of disruption. This allows an adaptive system to tradeoff disruption cost with expected savings (or other benefits) induced by a potential adaptation in a principled, decision-theoretic fashion. We conducted two experiments with 48 participants to learn model parameters in an adaptive menu selection environment. We demonstrate the utility of our approach in simulation and usability studies. Usability results with 8 participants suggest that our approach is competitive with other adaptive menus w.r.t. task performance, while providing the ability to reduce disruption and adapt to user preferences.

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Cited By

Eloi DSahraoui AVanderdonckt JLeiva L(2024)User-controlled Form Adaptation by Unsupervised LearningAdjunct Proceedings of the 2024 Nordic Conference on Human-Computer Interaction10.1145/3677045.3685431(1-8)Online publication date: 13-Oct-2024
https://dl.acm.org/doi/10.1145/3677045.3685431
Sluÿters AVanderdonckt JVatavu R(2021)Engineering Slidable Graphical User Interfaces with SlimeProceedings of the ACM on Human-Computer Interaction10.1145/34571475:EICS(1-29)Online publication date: 29-May-2021
https://dl.acm.org/doi/10.1145/3457147
Todi KBailly GLeiva LOulasvirta AKitamura YQuigley AIsbister KIgarashi TBjørn PDrucker S(2021)Adapting User Interfaces with Model-based Reinforcement LearningProceedings of the 2021 CHI Conference on Human Factors in Computing Systems10.1145/3411764.3445497(1-13)Online publication date: 6-May-2021
https://dl.acm.org/doi/10.1145/3411764.3445497
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Index Terms

A probabilistic mental model for estimating disruption
1. Computing methodologies
  1. Artificial intelligence
2. Human-centered computing

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Published In

cover image ACM Conferences

IUI '09: Proceedings of the 14th international conference on Intelligent user interfaces

February 2009

522 pages

ISBN:9781605581682

DOI:10.1145/1502650

General Chairs:
Cristina Conati
University of British Columbia, Canada
,
Mathias Bauer
mineway GmbH, Germany
,
Program Chairs:
Nuria Oliver
Telefonica Research, Spain
,
Dan Weld
University of Washington, USA

Copyright © 2009 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: 08 February 2009

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IUI09

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IUI09: 14th International Conference on Intelligent User Interfaces

February 8 - 11, 2009

Florida, Sanibel Island, USA

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Overall Acceptance Rate 746 of 2,811 submissions, 27%

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30th International Conference on Intelligent User Interfaces

March 24 - 27, 2025

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Cited By

Eloi DSahraoui AVanderdonckt JLeiva L(2024)User-controlled Form Adaptation by Unsupervised LearningAdjunct Proceedings of the 2024 Nordic Conference on Human-Computer Interaction10.1145/3677045.3685431(1-8)Online publication date: 13-Oct-2024
https://dl.acm.org/doi/10.1145/3677045.3685431
Sluÿters AVanderdonckt JVatavu R(2021)Engineering Slidable Graphical User Interfaces with SlimeProceedings of the ACM on Human-Computer Interaction10.1145/34571475:EICS(1-29)Online publication date: 29-May-2021
https://dl.acm.org/doi/10.1145/3457147
Todi KBailly GLeiva LOulasvirta AKitamura YQuigley AIsbister KIgarashi TBjørn PDrucker S(2021)Adapting User Interfaces with Model-based Reinforcement LearningProceedings of the 2021 CHI Conference on Human Factors in Computing Systems10.1145/3411764.3445497(1-13)Online publication date: 6-May-2021
https://dl.acm.org/doi/10.1145/3411764.3445497
Abrahão SInsfran ESluÿters AVanderdonckt J(2021)Model-based intelligent user interface adaptation: challenges and future directionsSoftware and Systems Modeling10.1007/s10270-021-00909-7Online publication date: 16-Jul-2021
https://doi.org/10.1007/s10270-021-00909-7
Vanderdonckt JZen MVatavu R(2019)AB4WebProceedings of the ACM on Human-Computer Interaction10.1145/33311603:EICS(1-28)Online publication date: 13-Jun-2019
https://dl.acm.org/doi/10.1145/3331160
Kiefer PGiannopoulos IAthanasios Anagnostopoulos VSchöning JRaubal M(2017)Controllability mattersGeoinformatica10.1007/s10707-016-0282-x21:3(619-641)Online publication date: 1-Jul-2017
https://dl.acm.org/doi/10.1007/s10707-016-0282-x
Rooke MGrossman TFitzmaurice GBrooks SIrani P(2011)AppMapProceedings of Graphics Interface 201110.5555/1992917.1992936(111-118)Online publication date: 25-May-2011
https://dl.acm.org/doi/10.5555/1992917.1992936
Ritter W(2011)Benefits of subliminal feedback loops in human-computer interactionAdvances in Human-Computer Interaction10.1155/2011/3464922011(1-11)Online publication date: 1-Jan-2011
https://dl.acm.org/doi/10.1155/2011/346492

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