<inline-formula><tex-math notation="LaTeX">$Q$</tex-math></inline-formula>-Mapping: Learning User-Preferred Operation Mappings With Operation-Action Value Function

Riki Satogata, Mitsuhiko Kimoto, Yosuke Fukuchi, Kohei Okuoka, Michita Imai

Research output: Contribution to journalArticlepeer-review

Abstract

User interfaces have been designed to fit typical users and their usage styles as assumed by designers. However, it is impossible to cover all the possible use cases. To address this problem, we propose <inline-formula><tex-math notation="LaTeX">$Q$</tex-math></inline-formula>-Mapping, which is a method for user interfaces to acquire the operation mapping, or mapping from user operations to their effects. <inline-formula><tex-math notation="LaTeX">$Q$</tex-math></inline-formula>-Mapping has an advantage over previous techniques in that it can acquire operation mapping interactively. The core idea of <inline-formula><tex-math notation="LaTeX">$Q$</tex-math></inline-formula>-Mapping is that what a user selects as an ideal action has a tendency to be the same as the action that has the highest <inline-formula><tex-math notation="LaTeX">$Q$</tex-math></inline-formula>-value. On the basis of this concept, we defined the operation-action value function, which can be calculated from the value that a user expects to gain when a particular mapping is given in that state and is updated each time an operation occurs. We conducted a simulation experiment and a user study to investigate the <inline-formula><tex-math notation="LaTeX">$Q$</tex-math></inline-formula>-Mapping performance and the effects of the acquisition of interactive operation mapping. The simulation results showed that the changeability of operation mapping could be controlled by a coefficient called the balancing parameter. As for the user study, we found that <inline-formula><tex-math notation="LaTeX">$Q$</tex-math></inline-formula>-Mapping with a balancing parameter that decays with time was able to acquire operation mapping that was easy for users to understand. These results demonstrate the importance of balancing consistency and adaptability in the interactive acquisition of operation mapping.

Original languageEnglish
Pages (from-to)1-13
Number of pages13
JournalIEEE Transactions on Human-Machine Systems
DOIs
Publication statusAccepted/In press - 2022

Keywords

  • <inline-formula xmlns:ali="http://www.niso.org/schemas/ali/1.0/" xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink" xmlns:xsi="http://www.w3.org/2001/XMLSchema-instance"> <tex-math notation="LaTeX">$Q$</tex-math> </inline-formula>-learning
  • Control systems
  • Human–computer interface
  • Keyboards
  • Legged locomotion
  • Man-machine systems
  • Performance evaluation
  • Q-learning
  • Task analysis
  • human–device interaction

ASJC Scopus subject areas

  • Human Factors and Ergonomics
  • Control and Systems Engineering
  • Signal Processing
  • Human-Computer Interaction
  • Computer Science Applications
  • Computer Networks and Communications
  • Artificial Intelligence

Fingerprint

Dive into the research topics of '<inline-formula><tex-math notation="LaTeX">$Q$</tex-math></inline-formula>-Mapping: Learning User-Preferred Operation Mappings With Operation-Action Value Function'. Together they form a unique fingerprint.

Cite this