AI – Based Approach for Mobile User Interface Adaptation

Software: Practice and Experience, 2005

Wearable, handheld, and embedded or standalone intelligent devices are becoming quite common and can support a diverse range of applications. In order to simplify development of applications which can adapt to a variety of mobile devices, we propose an adaptation framework which includes three techniques: follow-me, context-aware adaptation, and remote control scheme. For the first, we construct a personal agent capable of carrying its owner's applications. Second, we design a personal agent capable of carrying applications with an adaptable hierarchical structure. Then, applications can be adapted approximately to the context of devices by using an attribute-based component decision algorithm. Finally, to achieve a remote control scheme, we distribute the computational load of applications on the resource-restricted mobile devices. An application is divided into two parts that can be executed on a user device and a server separately. In short, this framework facilitates the development of widespread applications for ubiquitous computing environments. Furthermore, it enables the applications to follow their owners and automatically adapt to different devices.

IEEE International Conference on Mobile Data Management, 2004. Proceedings. 2004, 2004

This paper discusses the problem of content adaptation for mobile devices. The adaptation considers the context of the client and also the environment where the client request is received. A device independent model is defined and used in order to achieve automatic adaptation of the content based on its semantic and the capabilities of the target device. Our system includes a context description model and a client repository and offers device contexts management and querying functions. The proposed system uses the XQuery language to query the profiles and delivers the results in the form of SOAP services.

Proceedings of the companion publication of the 2013 international conference on Intelligent user interfaces companion - IUI '13 Companion, 2013

With the growing need for intelligent software, exploring the potential of Machine Learning (ML) algorithms for User Interface (UI) adaptation becomes an ultimate requirement. The work reported in this paper aims at enhancing the UI interaction by using a Rule Management Engine (RME) in order to handle a training phase for personalization. This phase is intended to teach to the system novel adaptation strategies based on the end-user feedback concerning his interaction (history, preferences…). The goal is also to ensure an adaptation learning by capitalizing on the user feedbacks via a promoting/demoting technique, and then to employ it later in different levels of the UI development.

Increasingly large number of the applications installed on smartphones tends to harm the application lookup efficiency. In this paper, we introduce Nihao, a personalized intelligent app launcher system, which could help the users to find apps quickly. Nihao predicts which app the user will likely open next based on a Bayesian Network model leveraging the contextual information such as the time of day, the day of week, the user's location and the last used app with the hypothesis that the users' app usage pattern is context dependent. Through the field study with seven users over six weeks, we first validate the above hypothesis by comparing the prediction accuracy of Nihao with other predictors. We found that the larger UI change did not necessarily yield longer app lookup time as the app lookup time highly depended on the app icon position on screen, which suggested the prediction accuracy was the most important factor in designing such a system. At the end of the study, we conducted a user survey to evaluate Nihao qualitatively. The survey results show that five out of seven users were quite satisfied with the prediction of Nihao and thought it could help to save both app lookup and management time by ranking the app icons automatically while Nihao did not help the other two users much since they used their phones primarily for calling and texting (not for apps).

Concepts, Methodologies, Tools, and Applications

In this chapter, the practical issue of realizing a necessary intelligence quotient for conceiving intelligent user interfaces (IUIs) on mobile devices is considered. Mobile computing scenarios differ radically from the normal fixed workstation environment that most people are familiar with. It is in this dynamicity and complexity that the key motivations for realizing IUIs on mobile devices may be found. Thus, the chapter initially motivates the need for the deployment of IUIs in mobile contexts by reflecting on the archetypical elements that comprise the average mobile user’s situation or context. A number of broad issues pertaining to the deployment of AI techniques on mobile devices are considered before a practical realisation of this objective through the intelligent agent paradigm is presented. It is the authors hope that a mature understanding of the mobile computing usage scenario, augmented with key insights into the practical deployment of AI in mobile scenarios, will aid...

2012 Conference on Technologies and Applications of Artificial Intelligence, 2012

Due to the proliferation of mobile applications (abbreviated as Apps) on smart phones, users can install many Apps to facilitate their life. Usually, users browse their Apps by swiping touch screen on smart phones, and are likely to spend much time on browsing Apps. In this paper, we design an AppNow widget that is able to predict users' Apps usage. Therefore, users could simply execute Apps from the widget. The main theme of this paper is to construct the temporal profiles which identify the relation between Apps and their usage times. In light of the temporal profiles of Apps, the AppNow widget predicts a list of Apps which are most likely to be used at the current time. In our experiments, we collected real usage traces to show that the accuracy of AppNow could reach 86% for identifying temporal profiles and 90% for predicting App usage.

Lecture Notes in Computer Science, 2002

Device characteristics, such as screen size and means of interaction, and the context in which a device is used, seriously affect the user's mental representation of an information environment and its intended use. We hypothesize that user characteristics are valuable resources for determining which information is of interest in specific situations. Our project goal is to design mechanisms for adapting navigation support to device characteristics and its context of use, thereby considering that user goals and the resulting expected navigation behavior might be subject to change.

wmcsa, 2000

Concepts, Methodologies, Tools, and Applications

Adaptive services based on context-awareness are considered to be a precious benefit of mobile applications. Effective adaptations however, have to be based on critical context criteria. For example, presence and availability mechanisms enable the system to decide when the user is in a certain locale and whether the user is available to engage in certain actions. What is even more challenging is a personalization of the user interface to the interests and preferences of the individual user and the characteristics of the used end device. Multimedia personalization is concerned with the building of an adaptive multimedia system that can customize the representation of multimedia content to the needs of a user. Mobile multimedia personalization especially, is related with the particular features of mobile devices' usage. In order to fully support customization processes, a personalization perspective is essential to classify the multimedia interface elements and to analyze their influence on the effectiveness of mobile applications.

Mobile applications are becoming increasingly widespread and complex. Many of these applications suffer from usability issues, including information overload, screen clutter, lack of task support and limited interaction mechanisms. Adaptive user interfaces (AUIs) have been proposed to address some of these usability issues. The aim of this paper is to investigate how AUIs can improve the usability of mobile applications. This paper discusses several simple types of adaptation that have been shown to yield significant usability benefits for mobile applications. Two case studies are presented to illustrate how an AUI can be incorporated into different types of mobile applications. This paper also discusses the lessons learned from these case studies and presents some implications for designing adaptive systems in the future.