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Supporting Adaptive Interfaces in a Knowledge-Based User Interface Environment

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This paper explores the design and implementation of adaptive interfaces within knowledge-based user environments. It emphasizes the importance of user modelling and adaptation strategies to enhance interaction experiences. Drawing from the expertise of leading researchers, it discusses various techniques for personalizing user interfaces, including command sequences, pattern recognition, and collaborative dialogue. The role of built-in knowledge in adaptive systems is also examined, alongside the significance of user control and responsiveness. This comprehensive analysis aims to improve user satisfaction and efficiency in computational tasks.

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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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