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

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Research units


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)
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


ConferenceInternational Conference on Intelligent User Interfaces
Abbreviated titleIUI
CountryUnited States
CityLos Angeles

    Research areas

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

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