Interaction Design With Multi-objective Bayesian Optimization

Yi Chi Liao, John J. Dudley, George B. Mo, Chun Lien Cheng, Liwei Chan, Antti Oulasvirta, Per Ola Kristensson

Research output: Contribution to journalArticleScientificpeer-review

8 Citations (Scopus)


Interaction design typically involves challenging decision making that requires designers to consider multiple parameters and careful tradeoffs between various objectives. This article examines how AI can facilitate the process of interaction design by offloading some of the complex decision making required of designers. We study how multi-objective Bayesian optimization can be used to support designers when creating a tactile display for smart watches. We present the results of a study that explores how such human-AI collaboration afforded by multi-objective Bayesian optimization can be exploited by designers, and the advantages and disadvantages this solution offers over conventional design practice.

Original languageEnglish
Pages (from-to)29-38
Number of pages10
JournalIEEE Pervasive Computing
Issue number1
Early online date23 Jan 2023
Publication statusPublished - 2023
MoE publication typeA1 Journal article-refereed


  • Bayes methods
  • Conferences
  • Haptic interfaces
  • Optimization
  • Prototypes
  • Task analysis
  • Vibrations


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