Title data
Buschek, Daniel:
Probabilistic UI Representation and Reasoning in Touch Interfaces.
In: Williamson, John H. ; Oulasvirta, Antti ; Kristensson, Per Ola ; Banovic, Nikola
(ed.):
Bayesian Methods for Interaction and Design. -
Cambridge, UK ; New York, USA
: Cambridge University Press
,
2022
. - pp. 163-187
ISBN 978-1-108-79270-7
DOI: https://doi.org/10.1017/9781108874830
Project information
Project title: |
Project's official title Project's id AI Tools - Continuous Interaction with Computational Intelligence Tools No information |
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Abstract in another language
This chapter describes and reflects on the ProbUI framework as an example of a probabilistic alternative to the representation of input behaviour in graphical user interfaces (GUIs), in particular for touch: Concretely, ProbUI treats touch input as uncertain and decouples the GUI elements' visuals from their internal representation of user behaviour. This opens up three generalisations beyond the current standard of bounding boxes, namely allowing for probabilistic handling of input behaviour, multiple such behaviour representations per GUI element, and sequential models. The chapter reflects on ProbUI and more broadly on probabilistic approaches for building GUIs, extracting a generalised perspective of viewing GUIs as a method for structuring signal and noise in an abstract input behaviour space. The chapter concludes with implications for the design of (touch) GUIs to improve usability and user experience, in particular by building adaptive GUIs that reason about, and react to, ongoing user input behaviour
Further data
Item Type: | Article in a book |
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Refereed: | Yes |
Institutions of the University: | Faculties > Faculty of Mathematics, Physics und Computer Science > Department of Computer Science Faculties Faculties > Faculty of Mathematics, Physics und Computer Science |
Result of work at the UBT: | Yes |
DDC Subjects: | 000 Computer Science, information, general works > 004 Computer science |
Date Deposited: | 10 Nov 2022 12:10 |
Last Modified: | 10 Nov 2022 12:10 |
URI: | https://eref.uni-bayreuth.de/id/eprint/72744 |