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

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Presentation on theme: "Supporting Adaptive Interfaces in a Knowledge-Based User Interface Environment Piyawadee Noi Sukaviriya James Foley."— Presentation transcript:

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 TechnologyResearch Scientist II doctoral degree from the George Washington University Mobile Solutions/Personal Systems IBM Thomas J Watson Research Center CHI '97 Workshop: Speech User Interface Design ChallengesCHI '97 Workshop: Speech User Interface Design Challenges

3 James Foley Associate Dean, College of Computing Georgia Institute of Technology Associate Dean chairman of the Computing Research Association 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 LeedsUniversity of Leeds Pattern identification and machine learning in human-computer interaction

9 Barbara Smith Professor - University of Huddersfield, West Yorkshire, U.K. Professor 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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