RL-KLM: Automating Keystroke-level Modeling with Reinforcement Learning

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

2 Citations (Scopus)
199 Downloads (Pure)

Abstract

The Keystroke-Level Model (KLM) is a popular model for predicting users’ task completion times with graphical user interfaces. KLM predicts task completion times as a linear function of elementary operators. However, the policy, or the assumed sequence of the operators that the user executes, needs to be prespecified by the analyst. This paper investigates Reinforcement Learning (RL) as an algorithmic method to obtain the policy automatically. We define
the KLM as an Markov Decision Process, and show that when solved with RL methods, this approach yields user-like policies in simple but realistic interaction tasks. RL-KLM offers a quick way to obtain a global upper bound for user performance. It opens up new possibilities to use KLM in computational interaction. However, scalability and validity remain open issues.
Original languageEnglish
Title of host publication24th International Conference on Intelligent User Interfaces (IUI ’19)
PublisherACM
Pages476-480
Number of pages5
ISBN (Electronic)978-1-4503-6272-6
DOIs
Publication statusPublished - 2019
MoE publication typeA4 Article in a conference publication
EventInternational Conference on Intelligent User Interfaces - Los Angeles, United States
Duration: 16 Mar 201920 Mar 2019

Conference

ConferenceInternational Conference on Intelligent User Interfaces
Abbreviated titleIUI
CountryUnited States
CityLos Angeles
Period16/03/201920/03/2019

Keywords

  • Keystroke-level modelling
  • Reinforcement Learning
  • Computational evaluation
  • Computational design

Fingerprint Dive into the research topics of 'RL-KLM: Automating Keystroke-level Modeling with Reinforcement Learning'. Together they form a unique fingerprint.

Cite this