| Abstract |
User Interfaces (UIs) constitute the prominent means for interacting with computing systems and
applications. Designing suitable, user-friendly UIs poses a multitude of challenges, given the
heterogeneity of potential users and contexts of use. This variability cannot be handled by a onesize-fits-all approach, but needs to be addressed by adapting the UI so that it is tailored to the
current user and context. Existing approaches are mainly focused on design-time or one-off
adaptation of the UI at startup, as opposed to real-time continuous adaptation based on the
current situation. However, UIs are nowadays increasingly being used in continuously changing
contexts, such as in mobile and Extended Reality (XR) applications, calling for more dynamic
approaches.
The majority of research approaches regarding adaptive Graphical User Interfaces (GUIs) is
primarily concerned with the development of handcrafted rule sets and heuristics. Albeit in recent
years, Combinatorial Optimization has emerged as a powerful and flexible tool for the
computational generation and adaptation of GUIs, providing a coherent formalism for expressing
and analyzing design decisions. In general, this method treats interface adaptation and generation
as an optimization problem, by defining constraints and maximizing (or minimizing) an objective
function that represents the goal of the UI, for instance, maximizing the interface’s usability, or
minimizing user effort. However, in existing approaches, the parameters of the optimization
problem are manually specified or static, and do not reflect run-time changes in the current
context of use. In addition, different types of design problems in a given UI, such as the selection
of its GUI components and its layout, are solved separately and independently.
A key UI design consideration in many application domains, such as healthcare, aviation and the
military, is Situational Awareness (SA), playing a major role in risk management and safety. It refers
to the human perception and understanding of the environment and the current situation, as well
as the human ability to predict how they will evolve. In this work, a novel computational approach
for the dynamic adaptation of UIs is proposed, which aims at enhancing the SA of users by
leveraging the current context and providing the most useful information, in an optimal and
efficient manner. By combining Ontology modeling and reasoning with Combinatorial
Optimization, the system decides what information to present, when to present it, where to
visualize it in the display - and how, taking into consideration contextual factors as well as
placement constraints. The main objective of the proposed approach is to optimize the SA
associated with the displayed UI at run-time, while avoiding information overload and induced
stress. In this respect, contrary to existing approaches, parameters of the optimization problem
are dynamically inferred, based on the current situation. Additionally, the design problems of GUI
component selection and UI layout are solved simultaneously, exploiting interrelationships.
Our proposed methodology is general-purpose, applicable to different platforms and domains,
including desktop, mobile and XR applications, for a variety of potential end-users. In the context
of this work, we have deployed our computational approach to the use case of an Augmented
Reality (AR) system for Law Enforcement Agents (LEAs). In order to extract user requirements and
model our application domain, co-creation workshops with end-users have been organized,
gaining insights into context factors that impact the SA of LEAs, and identifying GUI components
that would increase their SA during policing in different tasks and contexts. To explore the benefits
and limitations of the developed system, two evaluations have been conducted. The first one was
an expert-based evaluation with LEAs and User Experience (UX) experts, assessing the
appropriateness of the system’s decisions. The second one was a user-based evaluation involving
LEAs from different agencies, estimating the SA, the mental workload and the overall UX
associated with the system, through an AR simulation. The results indicate that the system
improves the observed and perceived user SA, by 9.25% and 25.63% respectively.
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