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

No Thumbnail Available
Journal Title
Journal ISSN
Volume Title
Conference article in proceedings
Date
2019
Major/Subject
Mcode
Degree programme
Language
en
Pages
5
476-480
Series
24th International Conference on Intelligent User Interfaces (IUI ’19)
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.
Description
| openaire: EC/H2020/637991/EU//COMPUTED
Keywords
Keystroke-level modelling, Reinforcement Learning, Computational evaluation, Computational design
Other note
Citation
Leino , K , Oulasvirta , A & Kurimo , M 2019 , RL-KLM: Automating Keystroke-level Modeling with Reinforcement Learning . in 24th International Conference on Intelligent User Interfaces (IUI ’19) . ACM , pp. 476-480 , International Conference on Intelligent User Interfaces , Los Angeles , California , United States , 16/03/2019 . https://doi.org/10.1145/3301275.3302285