Investigating Positive and Negative Qualities of Human-in-the-Loop Optimization for Designing Interaction Techniques

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

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

Abstract

Designers reportedly struggle with design optimization tasks where they are asked to find a combination of design parameters that maximizes a given set of objectives. In HCI, design optimization problems are often exceedingly complex, involving multiple objectives and expensive empirical evaluations. Model-based computational design algorithms assist designers by generating design examples during design, however they assume a model of the interaction domain. Black box methods for assistance, on the other hand, can work with any design problem. However, virtually all empirical studies of this human-in-the-loop approach have been carried out by either researchers or end-users. The question stands out if such methods can help designers in realistic tasks. In this paper, we study Bayesian optimization as an algorithmic method to guide the design optimization process. It operates by proposing to a designer which design candidate to try next, given previous observations. We report observations from a comparative study with 40 novice designers who were tasked to optimize a complex 3D touch interaction technique. The optimizer helped designers explore larger proportions of the design space and arrive at a better solution, however they reported lower agency and expressiveness. Designers guided by an optimizer reported lower mental effort but also felt less creative and less in charge of the progress. We conclude that human-in-the-loop optimization can support novice designers in cases where agency is not critical.

Original languageEnglish
Title of host publicationCHI 2022 - Proceedings of the 2022 CHI Conference on Human Factors in Computing Systems
PublisherAssociation for Computing Machinery (ACM)
Number of pages14
ISBN (Electronic)9781450391573
DOIs
Publication statusPublished - 28 Apr 2022
MoE publication typeA4 Article in a conference publication
EventACM SIGCHI Annual Conference on Human Factors in Computing Systems - New Orleans, United States
Duration: 30 Apr 20225 May 2022

Publication series

NameConference on Human Factors in Computing Systems - Proceedings

Conference

ConferenceACM SIGCHI Annual Conference on Human Factors in Computing Systems
Abbreviated titleACM CHI
Country/TerritoryUnited States
CityNew Orleans
Period30/04/202205/05/2022

Keywords

  • Bayesian Optimization
  • Haptics
  • Human-in-the-loop Optimization
  • Interface Design
  • Multi-objective Optimization
  • Touch

Fingerprint

Dive into the research topics of 'Investigating Positive and Negative Qualities of Human-in-the-Loop Optimization for Designing Interaction Techniques'. Together they form a unique fingerprint.

Cite this