supporting adaptive interfaces in a knowledge-based user interface environment
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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 - PowerPoint PPT PresentationTRANSCRIPT
Supporting Adaptive Interfaces in a Knowledge-Based User Interface EnvironmentPiyawadee Noi Sukaviriya
James Foley
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
James Foley
Associate Dean, College of Computing Georgia Institute of Technology
chairman of the Computing Research Association
User Interface Design Environment Adaptive Interface User Model Adaptation Strategy
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
Adaptation Strategy
Suggestions; user has control Reorganizations made less frequently
Role of Built-in Knowledge in Adaptive Interface SystemsDaniel Crow
Barbara Smith
Daniel Crow
School of Computer Studies – University of Leeds
Pattern identification and machine learning in human-computer interaction
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
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
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