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Adapting User Interfaces with Model-based Reinforcement Learning

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Antti OulasvirtaAuthors Info & Claims

CHI '21: Proceedings of the 2021 CHI Conference on Human Factors in Computing Systems

Article No.: 573, Pages 1 - 13

https://doi.org/10.1145/3411764.3445497

Published: 07 May 2021 Publication History

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Abstract

Adapting an interface requires taking into account both the positive and negative effects that changes may have on the user. A carelessly picked adaptation may impose high costs to the user – for example, due to surprise or relearning effort – or “trap” the process to a suboptimal design immaturely. However, effects on users are hard to predict as they depend on factors that are latent and evolve over the course of interaction. We propose a novel approach for adaptive user interfaces that yields a conservative adaptation policy: It finds beneficial changes when there are such and avoids changes when there are none. Our model-based reinforcement learning method plans sequences of adaptations and consults predictive HCI models to estimate their effects. We present empirical and simulation results from the case of adaptive menus, showing that the method outperforms both a non-adaptive and a frequency-based policy.

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Cited By

Vidmanov DAlfimtsev A(2024)Mobile User Interface Adaptation Based on Usability Reward Model and Multi-Agent Reinforcement LearningMultimodal Technologies and Interaction10.3390/mti80400268:4(26)Online publication date: 24-Mar-2024
https://doi.org/10.3390/mti8040026
Daish PMicallef NLorenzo-Dus NPaiement ASahoo D(2024)Towards Co-designing a Human-Agent Interface: A Case Study in Online Grooming Detection.Proceedings of the 12th International Conference on Human-Agent Interaction10.1145/3687272.3690893(390-392)Online publication date: 24-Nov-2024
https://dl.acm.org/doi/10.1145/3687272.3690893
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
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Index Terms

Adapting User Interfaces with Model-based Reinforcement Learning
1. Computing methodologies
2. Human-centered computing
  1. Human computer interaction (HCI)
    1. Interaction paradigms

Index terms have been assigned to the content through auto-classification.

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cover image ACM Conferences

CHI '21: Proceedings of the 2021 CHI Conference on Human Factors in Computing Systems

May 2021

10862 pages

ISBN:9781450380966

DOI:10.1145/3411764

General Chairs:
Yoshifumi Kitamura
Tohoku University, Japan
,
Aaron Quigley
University of New South Wales, Australia
,
Program Chairs:
Katherine Isbister
University of California Santa Cruz, USA
,
Takeo Igarashi
The University of Tokyo, Japan
,
Publications Chairs:
Pernille Bjørn
University of Copenhagen, Denmark
,
Steven Drucker
Microsoft Research, USA

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Published: 07 May 2021

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Cited By

Vidmanov DAlfimtsev A(2024)Mobile User Interface Adaptation Based on Usability Reward Model and Multi-Agent Reinforcement LearningMultimodal Technologies and Interaction10.3390/mti80400268:4(26)Online publication date: 24-Mar-2024
https://doi.org/10.3390/mti8040026
Daish PMicallef NLorenzo-Dus NPaiement ASahoo D(2024)Towards Co-designing a Human-Agent Interface: A Case Study in Online Grooming Detection.Proceedings of the 12th International Conference on Human-Agent Interaction10.1145/3687272.3690893(390-392)Online publication date: 24-Nov-2024
https://dl.acm.org/doi/10.1145/3687272.3690893
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
Langerak TChristen SAlbaba MGebhardt CHolz CHilliges O(2024)MARLUI: Multi-Agent Reinforcement Learning for Adaptive Point-and-Click UIsProceedings of the ACM on Human-Computer Interaction10.1145/36611478:EICS(1-27)Online publication date: 17-Jun-2024
https://dl.acm.org/doi/10.1145/3661147
Gaspar-Figueiredo DFernández-Diego MNuredini RAbrahao SInsfran E(2024)Reinforcement Learning-Based Framework for the Intelligent Adaptation of User InterfacesCompanion Proceedings of the 16th ACM SIGCHI Symposium on Engineering Interactive Computing Systems10.1145/3660515.3661329(40-48)Online publication date: 24-Jun-2024
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Lu FChen MHsu HDeshpande PWang CMacIntyre B(2024)Adaptive 3D UI Placement in Mixed Reality Using Deep Reinforcement LearningExtended Abstracts of the CHI Conference on Human Factors in Computing Systems10.1145/3613905.3651059(1-7)Online publication date: 11-May-2024
https://dl.acm.org/doi/10.1145/3613905.3651059
Park GPanda PTankelevitch LRintel S(2024)CoExplorer: Generative AI Powered 2D and 3D Adaptive Interfaces to Support Intentionality in Video MeetingsExtended Abstracts of the CHI Conference on Human Factors in Computing Systems10.1145/3613905.3650797(1-10)Online publication date: 11-May-2024
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Bai YIkkala AOulasvirta AZhao SWang LYang PXu P(2024)Heads-Up Multitasker: Simulating Attention Switching On Optical Head-Mounted DisplaysProceedings of the 2024 CHI Conference on Human Factors in Computing Systems10.1145/3613904.3642540(1-18)Online publication date: 11-May-2024
https://dl.acm.org/doi/10.1145/3613904.3642540
Zhang YTennekes MDe Jong TCurier LCoecke BChen M(2024)Simulation-based Optimization of User Interfaces for Quality-assuring Machine Learning Model PredictionsACM Transactions on Interactive Intelligent Systems10.1145/359455214:1(1-32)Online publication date: 9-Jan-2024
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