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Probabilistic UI Representation and Reasoning in Touch Interfaces

Titelangaben

Buschek, Daniel:
Probabilistic UI Representation and Reasoning in Touch Interfaces.
In: Williamson, John H. ; Oulasvirta, Antti ; Kristensson, Per Ola ; Banovic, Nikola (Hrsg.): Bayesian Methods for Interaction and Design. - Cambridge, UK ; New York, USA : Cambridge University Press , 2022 . - S. 163-187
ISBN 978-1-108-79270-7
DOI: https://doi.org/10.1017/9781108874830

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Projekttitel:
Offizieller Projekttitel
Projekt-ID
AI Tools - Continuous Interaction with Computational Intelligence Tools
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Abstract

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

Weitere Angaben

Publikationsform: Aufsatz in einem Buch
Begutachteter Beitrag: Ja
Institutionen der Universität: Fakultäten > Fakultät für Mathematik, Physik und Informatik > Institut für Informatik
Fakultäten
Fakultäten > Fakultät für Mathematik, Physik und Informatik
Titel an der UBT entstanden: Ja
Themengebiete aus DDC: 000 Informatik,Informationswissenschaft, allgemeine Werke > 004 Informatik
Eingestellt am: 10 Nov 2022 12:10
Letzte Änderung: 10 Nov 2022 12:10
URI: https://eref.uni-bayreuth.de/id/eprint/72744