Anil Shankar

Interactive Genetic Algorithms for User Interface Design

We attack the problem of user fatigue in using an interactive genetic algorithm to evolve user interfaces in the XUL interface definition language. The interactive genetic algorithm combines computable user interface design metrics with subjective user input to guide evolution. Individuals in our population represent interface specifications and we compute an individuals fitness from a weighted combination of user input and user interface design guidelines. Results from our preliminary study involving three users indicate that users are able to effectively bias evolution towards user interface designs that reflect both user preferences and computed guideline metrics. Furthermore, we can reduce fatigue, defined by the number of choices needing to be made by the human designer, by doing two things. First, asking the user to pick just two (the best and worst) user interfaces from among a subset of nine shown. Second, asking the user to make the choice once every t generations, instead of every single generation. Our goal is to provide interface designers with an interactive tool that can be used to explore innovation and creativity in the design space of user interfaces.

User-context for adaptive user interfaces

We present results from an empirical user-study with ten users which investigates if information from a users environment helps a user interface to personalize itself to individual users to better meet usability goals and improve user-experience. In our research we use a microphone and a web-camera to collect this information (user-context) from the vicinity of a subjects desktop computer. Sycophant, our context-aware calendaring application and research test-bed uses machine learning techniques to successfully predict a user-preferred alarm type. Discounting user identity and motion information significantly degrades Sycophants performance on the alarm prediction task. Our user study emphasizes the need for user-context for personalizable user interfaces which can better meet effectiveness and utility usability goals. Results from our study further demonstrate that contextual information helps adaptive interfaces to improve user-experience.

Context learning can improve user interaction

Current computer applications lack user context and do not learn to use this context to improve user interaction. In this paper we present Sycophant, a context learning calendar application program which learns a mapping from user-related contextual features to application actions. In this preliminary work, Sycophant achieves good accuracy in learning this mapping. In addition, we find that including external context such as the presence or absence of motion and speech provides better performance in learning accurate mappings.

XCS for Personalizing Desktop Interfaces

We investigate whether XCS, a genetic algorithm based learning classifier system, can harness information from a users environment to help desktop applications better personalize themselves to individual users. Specifically, we evaluate XCSs ability to predict user-preferred actions for a calendar and a media player. Results from three real-world user studies indicate that XCS significantly outperforms a decision-tree learner to successfully predict user preferences for these two desktop interfaces. Our results also show that removing external user-related contextual information degrades XCSs performance. This performance degradation emphasizes the need for desktop applications to access external contextual information to better learn user preferences. Our results highlight the potential for a learning classifier systems based approach for personalizing desktop applications to improve the quality of human-computer interaction.

Learning classifier systems for user context learning

Current computer applications and user interfaces lack user context and are not successful in learning user preferences to improve user interaction. We present Sycophant, a context learning calendaring application program which is designed to learn a mapping from user-related contextual features to reminder actions. In this paper, we consider the feasibility of using a genetics-based machine learning technique, XCS, for the purpose of learning this mapping from a set of context features to reminder actions as a predictive data-mining task. We compare XCSs performance with a decision tree algorithm on this learning task and show that XCS outperforms the decision tree learner.

Sycophant: An API for Research in Context-Aware User Interfaces

Research in context-aware user interfaces aims to improve human-computer interaction by providing more effective, smarter and user-friendlier solutions for computer applications. Currently, software available for performing such research and developing context-aware interfaces is very limited both in scope and possibilities of extension. Sycophant was designed with two objectives in mind: first, to allow easy insertion of new features and capabilities needed for conducting research and, second, to provide a reusable, readily available programming resource for developing new context-aware interactive software applications. Available as open source software, Sycophants API and the calendaring application we created using it are presented in this paper in terms of functional capabilities, high level architecture, detailed design, and results of use. Procedural steps for developing new context-aware user interfaces using our API are also described in the paper.

Software Environment for Research on Evolving User Interface Designs

We investigate the trade off between investing effort in improving the features of a research environment that increases productivity and investing such effort in actually conducting the research experiments using a less elaborated, albeit sufficiently operational environment. The study case presented is an interactive genetic algorithm environment we created to evolve user interfaces designs. We present three productivity improvements integrated in our environment and examine whether on the long run the research productivity can be in fact increased by spending development time on enhancing the research tools rather than on performing the research itself. The three improvements are the integration of the entire system interface into a main wxPython window, the addition of a runs manager for setting up multiple experiments, and the creation of a data manager for effective exploration and visualization of data produced in the experiment runs. We also discuss several guidelines for transitioning a research environment such as ours from a researchers tool to an end-users tool.