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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.
Original language | English |
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Title of host publication | CHI '21: Proceedings of the SIGCHI Conference on Human Factors in Computing Systems |
Subtitle of host publication | Making Waves, Combining Strengths |
Publisher | ACM |
Number of pages | 13 |
ISBN (Electronic) | 9781450380966 |
ISBN (Print) | 978-1-4503-8096-6 |
DOIs | |
Publication status | Published - 6 May 2021 |
MoE publication type | A4 Article in a conference publication |
Event | ACM SIGCHI Annual Conference on Human Factors in Computing Systems - Virtual, Online, Yokohama, Japan Duration: 8 May 2021 → 13 May 2021 https://chi2021.acm.org/ |
Conference
Conference | ACM SIGCHI Annual Conference on Human Factors in Computing Systems |
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Abbreviated title | ACM CHI |
Country/Territory | Japan |
City | Yokohama |
Period | 08/05/2021 → 13/05/2021 |
Internet address |
Keywords
- Adaptive User Interfaces
- Reinforcement Learning
- Predictive Models
- Monte Carlo Tree Search
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Dive into the research topics of 'Adapting User Interfaces with Model-based Reinforcement Learning'. Together they form a unique fingerprint.-
Human Automata: Simulator-based Methods for Collaborative AI
Oulasvirta, A., Dayama, N., Hegemann, L., Laine, M., Nioche, A., Shiripour, M., Putkonen, A., Kylmälä, J. & Santala, S.
01/01/2020 → 31/12/2023
Project: Academy of Finland: Other research funding
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Bayesian Artefact Design
Oulasvirta, A., Dayama, N., Hassinen, H., Leiva, L., Laine, M., Putkonen, A., Liao, Y., Peng, Z., Nioche, A., Shin, J. & Todi, K.
01/09/2018 → 31/08/2023
Project: Academy of Finland: Other research funding
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FCAI: Finnish Center for Artificial Intelligence (FCAI)
01/01/2019 → 31/12/2022
Project: Academy of Finland: Other research funding