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Computer-supported form design using keystroke-level modeling with reinforcement learning

Published: 16 March 2019 Publication History

Abstract

The Keystroke-Level Model (KLM) is commonly used to predict a user's task completion times with graphical user interfaces. With KLM, the user's behavior is modeled with a linear function of independent, elementary operators. Each task can be completed with a sequence of operators. The policy, or the assumed sequence that the user executes, is typically pre-specified by the analyst. Using Reinforcement Learning (RL), RL-KLM [4] proposes an algorithmic method to obtain this policy automatically. This approach yields user-like policies in simple but realistic interaction tasks, and offers a quick way to obtain an upper bound for user performance.
In this demonstration, we show how a policy is automatically learned by RL-KLM in form-filling tasks. A user can interact with the system by placing form fields onto a UI canvas. The system learns the fastest filling order for the form template according to Fitts' Law operators, and computes estimates the time required to complete the form. Attendees are able to iterate over their designs to see how the changes in designs affect user's policy and the task completion time.

References

[1]
Stuart K Card, Thomas P Moran, and Allen Newell. 1980. The keystroke-level model for user performance time with interactive systems. Commun. ACM 23, 7 (1980), 396--410.
[2]
Karim El Batran and Mark D Dunlop. 2014. Enhancing KLM (keystroke-level model) to fit touch screen mobile devices. In Proceedings of the 16th international conference on Human-computer interaction with mobile devices & services. ACM, 283--286.
[3]
Bonnie E John and David E Kieras. 1996. The GOMS family of user interface analysis techniques: Comparison and contrast. ACM Transactions on Computer-Human Interaction (TOCHI) 3, 4 (1996), 320--351.
[4]
Katri Leino, Antti Oulasvirta, and Mikko Kurimo. 2019. RL-KLM: Automating Keystroke-level Modeling with Reinforcement Learning. In 24rd International Conference on Intelligent User Interfaces. ACM.
[5]
I Scott MacKenzie. 1992. Fitts' law as a research and design tool in human-computer interaction. Human-computer interaction 7, 1 (1992), 91--139.

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  • (2024)MARLUI: Multi-Agent Reinforcement Learning for Adaptive Point-and-Click UIsProceedings of the ACM on Human-Computer Interaction10.1145/36611478:EICS(1-27)Online publication date: 17-Jun-2024
  • (2023)A Bayesian Approach for Quantifying Data Scarcity when Modeling Human Behavior via Inverse Reinforcement LearningACM Transactions on Computer-Human Interaction10.1145/355138830:1(1-27)Online publication date: 7-Mar-2023
  • (2022)Select or Suggest? Reinforcement Learning-based Method for High-Accuracy Target Selection on TouchscreensProceedings of the 2022 CHI Conference on Human Factors in Computing Systems10.1145/3491102.3517472(1-15)Online publication date: 29-Apr-2022
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cover image ACM Conferences
IUI '19 Companion: Companion Proceedings of the 24th International Conference on Intelligent User Interfaces
March 2019
173 pages
ISBN:9781450366731
DOI:10.1145/3308557
Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than the author(s) must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected].

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Publication History

Published: 16 March 2019

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Author Tags

  1. computational design
  2. computational evaluation
  3. keystroke-level modelling
  4. reinforcement learning

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  • European Research Council (ERC)

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IUI '19
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Overall Acceptance Rate 746 of 2,811 submissions, 27%

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Cited By

View all
  • (2024)MARLUI: Multi-Agent Reinforcement Learning for Adaptive Point-and-Click UIsProceedings of the ACM on Human-Computer Interaction10.1145/36611478:EICS(1-27)Online publication date: 17-Jun-2024
  • (2023)A Bayesian Approach for Quantifying Data Scarcity when Modeling Human Behavior via Inverse Reinforcement LearningACM Transactions on Computer-Human Interaction10.1145/355138830:1(1-27)Online publication date: 7-Mar-2023
  • (2022)Select or Suggest? Reinforcement Learning-based Method for High-Accuracy Target Selection on TouchscreensProceedings of the 2022 CHI Conference on Human Factors in Computing Systems10.1145/3491102.3517472(1-15)Online publication date: 29-Apr-2022
  • (2021)Adapting User Interfaces with Model-based Reinforcement LearningProceedings of the 2021 CHI Conference on Human Factors in Computing Systems10.1145/3411764.3445497(1-13)Online publication date: 6-May-2021
  • (2021)RL4HCI: Reinforcement Learning for Humans, Computers, and InteractionExtended Abstracts of the 2021 CHI Conference on Human Factors in Computing Systems10.1145/3411763.3441323(1-3)Online publication date: 8-May-2021
  • (2021)Usability of the login authentication process: passphrases and passwordsInformation & Computer Security10.1108/ICS-07-2021-009330:2(280-305)Online publication date: 30-Nov-2021

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