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

Tutkimustuotos: Artikkeli kirjassa/konferenssijulkaisussaConference contributionScientificvertaisarvioitu

5 Sitaatiot (Scopus)
408 Lataukset (Pure)


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.
Otsikko24th International Conference on Intelligent User Interfaces (IUI ’19)
ISBN (elektroninen)978-1-4503-6272-6
DOI - pysyväislinkit
TilaJulkaistu - 2019
OKM-julkaisutyyppiA4 Artikkeli konferenssijulkaisuussa
TapahtumaInternational Conference on Intelligent User Interfaces - Los Angeles, Yhdysvallat
Kesto: 16 maalisk. 201920 maalisk. 2019


ConferenceInternational Conference on Intelligent User Interfaces
KaupunkiLos Angeles


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