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Supporting Adaptive Interfaces in a Knowledge-Based User Interface Environment

Supporting Adaptive Interfaces in a Knowledge-Based User Interface Environment. Piyawadee Noi Sukaviriya James Foley. Piyawadee Noi Sukaviriya. was a Research Scientist II at Georgia Institute of Technology doctoral degree from the George Washington University

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Supporting Adaptive Interfaces in a Knowledge-Based User Interface Environment

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  1. Supporting Adaptive Interfaces in a Knowledge-Based User Interface Environment Piyawadee Noi Sukaviriya James Foley

  2. Piyawadee Noi Sukaviriya • was a Research Scientist II at Georgia Institute of Technology • doctoral degree from the George Washington University • Mobile Solutions/Personal SystemsIBM Thomas J Watson Research Center CHI '97 Workshop: Speech User Interface Design Challenges

  3. James Foley • Associate Dean, College of Computing Georgia Institute of Technology • chairman of the Computing Research Association

  4. User Interface Design Environment • Adaptive Interface • User Model • Adaptation Strategy

  5. Basic Construct of User Model • Using one interaction technique • How many successful uses • User cancels, requests help => difficulties • Which technique used to perform action • Repeated patterns • How long the user spends on each help session • Assumes technique used frequently is the preferred technique

  6. Adaptation Strategy • Suggestions; user has control • Reorganizations made less frequently

  7. Role of Built-in Knowledge in Adaptive Interface Systems Daniel Crow Barbara Smith

  8. Daniel Crow • School of Computer Studies – University of Leeds • Pattern identification and machine learning in human-computer interaction

  9. Barbara Smith • Professor - University of Huddersfield, West Yorkshire, U.K. • Interested in constraint programming and related fields of AI, specifically in Algorithms, Problems and Empirical Studies

  10. Task-Oriented Interfaces • DB_Habits – adaptive interface system • Uses command sequences • Use small amounts of low-level system knowledge • Adaptive - individual modelling with descriptive methods • Malleable – adaptation to individual user • Collaborative – interface as bridge between 2 agents

  11. User Modelling • Pattern recognition • Assumption: repeated sequences rep. tasks • Collaborative dialogue to confirm “task” • Identify equivalent commands in logfile • Fit between patterns and tasks • Pattern freqency • Order of tool usage

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