Sycophant User-Context Framework

Research Project of Anil Shankar, Ph.D Candidate, Computer Science and Engineering, University of Nevada, Reno, NV, 89557

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Journal Papers

Conference papers (Refereed only)

1. Shankar, Anil; Quiroz, Juan; Dascalu, Sergiu M.; Louis, Sushil J.; Nicolescu, Monica N., "Sycophant: An API for Research in Context-Aware User Interfaces," Software Engineering Advances, 2007. ICSEA 2007. International Conference on , vol., no., pp.83-83, 25-31 Aug. 2007

Abstract: 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, Sycophant's 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.

2. Quiroz, Juan; Shankar, Anil; Dascalu, Sergiu; Louis, Sushil "Software Environment for Research on Evolving User Interface Designs," Software Engineering Advances, 2007. ICSEA 2007. International Conference on , vol., no., pp.84-84, 25-31 Aug. 2007

Abstract: 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 researcher's tool to an end-user's tool.

3. Juan Quiroz, Sushil Louis, Anil Shankar and Sergiu Dascalu. Interactive Genetic Algorithms for User Interface Design. IEEE Congress on Evolutionary computation 2007

Abstract—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 in individual’s fitness from a weighted combination of user
input and user-interface-design guidelines. Results from our preliminary study involving three users indicates 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 n 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 and make it easier for end-users to further customize their interface without programming knowledge.

 

4. Shankar, A., Louis, S. J., Dascalu, S., Houmanfar, R., and Hayes, L. J. 2007. XCS for adaptive user-interfaces. In Proceedings of the 9th Annual Conference on Genetic and Evolutionary Computation (London, England, July 07 - 11, 2007). GECCO '07. ACM Press, New York, NY, 1876-1876.

Abstract: We outline our context learning framework that harnesses information from a user's environment to learn user preferences for application actions. Within this framework, we employ XCS in a real world application for personalizing user-interface actions to individual users. Sycophant, our context aware calendaring application and research test-bed, uses XCS to adaptively generate user-preferred alarms for ten users in our study. Our results show that XCS' alarm prediction performance equals or surpasses the performance of One-R and a decision tree algorithm for all the users. XCS' average performance is close to $90$ percent on the alarm prediction task for all ten users. These encouraging results further highlight the feasibility of using XCS for predictive data mining tasks and the promise of a classifier systems based approach to personalize user interfaces.

 

5. Shankar, A., Louis, S. J., Dascalu, S., Hayes, L. J., and Houmanfar, R. 2007. User-context for adaptive user interfaces. In Proceedings of the 12th international Conference on intelligent User interfaces (Honolulu, Hawaii, USA, January 28 - 31, 2007). IUI '07. ACM Press, New York, NY, 321-324.

Abstract: We present results from an empirical user-study with ten users which investigates if information from a user's 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 subject's 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 Sycophant's 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.

 

6. Shankar, A, Louis, S.J Better personalization using learning classifier systems In Proceedings of the 2nd Indian International Conference on Artificial Intelligence, Pune, India, December 20-22, 2005. IICAI 2005.

Abstract: This paper describes an approach for better application personalization by learning user-context. We present Sycophant, a context-sensitive calendaring application capable of generating four different types of reminders. Sycophant uses a Genetics-Based Machine Learning technique, XCS, to learn the type of reminder preferred by a user and successfully performs this user-context learning for three different users. XCS performs as well as a decision tree learner in predicting whether or not to generate a reminder and outperforms the decision tree learner in learning to predict the correct reminder.

 

7. Anil Shankar, Sushil J. Louis. Learning classifier systems for user context learning. In 2005 IEEE Congress on Evolutionary computation,September 2-5 2005, Edinburgh, UK , 2005.

Abstract::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 XCS’s performance with a decision tree algorithm on this learning task and show that XCS outper-forms the decision tree learner.

 

8. Sushil J. Louis, Anil Shankar. Context learning can improve user interaction. In Proceedings of the 2004 IEEE International Conference on information Reuse and Integration, IRI - 2004, November 8-10, 2004, las Vegas Hilton, Las Vegas, NV USA , pages 115-120, 2004.

Abstract: 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.

 

Theses

 

1. Dissertation Proposal, Fall 2006.

 

2. Master's Thesis: Simple User-Context for Better Application Personalization May2006.

Abstract: Current computer applications and user interfaces rely on sparse contextual information to learn user preferences. This thesis describes an approach to better learn user preferences using additional contextual information from cheap motion and speech sensors. We present Sycophant, our user-context-sensitive calendaring application. Sycophant is capable of generating four different types of reminders and primarily uses machine learning techniques for predicting the type of reminder a user prefers. To learn user preferences Sycophant maps user-related contextual features to reminder actions. We show that XCS, a genetics-based machine learning technique outperforms a well established decision tree learning algorithm on this mapping task.Our research indicates that additional external contextual information using motion and speech sensors improves Sycophant's performance for learning user preferences. Our approach for learning user context successfully generalizes across three different users.

 

3. Ph.D. Thesis, May 2008 

SYCOPHANT:  Context Based Generalized User Modeling Framework for
Desktop Applications

 

 

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