Communications of the ACM - Special issue on parallelism
Creating user interfaces by demonstration
Creating user interfaces by demonstration
Object lens: a “spreadsheet” for cooperative work
ACM Transactions on Information Systems (TOIS)
Intelligent user interfaces
EAGER: programming repetitive tasks by example
CHI '91 Proceedings of the SIGCHI Conference on Human Factors in Computing Systems
Demonstrational interfaces: Coming soon?
CHI '91 Proceedings of the SIGCHI Conference on Human Factors in Computing Systems
Intelligent interfaces as agents
Intelligent user interfaces
Anthropomorphism: from Eliza to Terminator 2
CHI '92 Proceedings of the SIGCHI Conference on Human Factors in Computing Systems
Automated customization of structure editors
International Journal of Man-Machine Studies - Special issue on structure-based editors and environments
Mondrian: a teachable graphical editor
Watch what I do
A learning interface agent for scheduling meetings
IUI '93 Proceedings of the 1st international conference on Intelligent user interfaces
Agents that reduce work and information overload
Communications of the ACM
Personal assistants: Direct manipulation vs. mixed initiative interfaces
International Journal of Human-Computer Studies
A model of user adoption of interface agents for email notification
Interacting with Computers
Planning for Reasoning with Multiple Common Sense Knowledge Bases
ACM Transactions on Interactive Intelligent Systems (TiiS) - Special Issue on Common Sense for Interactive Systems
Web Intelligence and Agent Systems
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Interface agents are computer programs that employ Artificial Intelligence techniques in order to provide assistance to a user dealing with a particular computer application. The paper discusses an interface agent which has been modelled closely after the metaphor of a personal assistant. The agent learns how to assist the user by (i) observing the user's actions and imitating them, (ii) receiving user feedback when it takes wrong actions and (iii) being trained by the user on the basis of hypothetical examples. The paper discusses how this learning agent was implemented using memory-based learning and reinforcement learning techniques. It presents actual results from two prototype agents built using these techniques: one for a meeting scheduling application and one for electronic mail. It argues that the machine learning approach to building interface agents is a feasible one which has several advantages over other approaches: it provides a customized and adaptive solution which is less costly and ensures better user acceptability. The paper also argues what the advantages are of the particular learning techniques used.