Adapting User Interfaces with Model-based Reinforcement Learning

Kashyap Todi, Luis Leiva, Gilles Bailly, Antti Oulasvirta

Research output: Chapter in Book/Report/Conference proceedingConference contributionScientificpeer-review

3 Citations (Scopus)
60 Downloads (Pure)

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 languageEnglish
Title of host publicationCHI '21: Proceedings of the SIGCHI Conference on Human Factors in Computing Systems
Subtitle of host publicationMaking Waves, Combining Strengths
PublisherACM
Number of pages13
ISBN (Electronic)9781450380966
ISBN (Print)978-1-4503-8096-6
DOIs
Publication statusPublished - 6 May 2021
MoE publication typeA4 Article in a conference publication
EventACM SIGCHI Annual Conference on Human Factors in Computing Systems - Virtual, Online, Japan
Duration: 8 May 202113 May 2021
https://chi2021.acm.org/

Conference

ConferenceACM SIGCHI Annual Conference on Human Factors in Computing Systems
Abbreviated titleACM CHI
Country/TerritoryJapan
CityVirtual, Online
Period08/05/202113/05/2021
Internet address

Keywords

  • Adaptive User Interfaces
  • Reinforcement Learning
  • Predictive Models
  • Monte Carlo Tree Search

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