4.7 Article

Combinatorial Optimization of Graphical User Interface Designs

期刊

PROCEEDINGS OF THE IEEE
卷 108, 期 3, 页码 434-464

出版社

IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
DOI: 10.1109/JPROC.2020.2969687

关键词

Grahical user interfaces; Task analysis; Linear programming; Multitasking; Human factors; Adversarial machine learning; Learning systems; Human computer interaction; Human factors; Computer applications; Combinatorial optimization; computational design; graphical user interfaces (GUIs); human-computer interaction (HCI); integer programming; interactive optimization; meta-heuristic optimization

资金

  1. European Research Council (ERC) through the European Union [637991]
  2. Academy of Finland

向作者/读者索取更多资源

The graphical user interface (GUI) has become the prime means for interacting with computing systems. It leverages human perceptual and motor capabilities for elementary tasks such as command exploration and invocation, information search, and multitasking. For designing a GUI, numerous interconnected decisions must be made such that the outcome strikes a balance between human factors and technical objectives. Normally, design choices are specified manually and coded within the software by professional designers and developers. This article surveys combinatorial optimization as a flexible and powerful tool for computational generation and adaptation of GUIs. As recently as 15 years ago, applications were limited to keyboards and widget layouts. The obstacle has been the mathematical definition of design tasks, on the one hand, and the lack of objective functions that capture essential aspects of human behavior, on the other. This article presents definitions of layout design problems as integer programming tasks, a coherent formalism that permits identification of problem types, analysis of their complexity, and exploitation of known algorithmic solutions. It then surveys advances in formulating evaluative functions for common design-goal foci such as user performance and experience. The convergence of these two advances has expanded the range of solvable problems. Approaches to practical deployment are outlined with a wide spectrum of applications. This article concludes by discussing the position of this application area within optimization and human-computer interaction research and outlines challenges for future work.

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