Machine learning of robot assembly plans
Machine learning of robot assembly plans
Learning internal representations by error propagation
Parallel distributed processing: explorations in the microstructure of cognition, vol. 1
Machine Learning
Generating production rules from decision trees
IJCAI'87 Proceedings of the 10th international joint conference on Artificial intelligence - Volume 1
LEAP: a learning apprentice for VLSI design
IJCAI'85 Proceedings of the 9th international joint conference on Artificial intelligence - Volume 1
Agents that reduce work and information overload
Communications of the ACM
Design and evaluation of a command recommendation system for software applications
ACM Transactions on Computer-Human Interaction (TOCHI)
Learning user preferences in distributed calendar scheduling
PATAT'04 Proceedings of the 5th international conference on Practice and Theory of Automated Timetabling
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Personalized knowledge-based systems have not yet become widespread, despite their potential for valuable assistance in many daily tasks. This is due, in part, to the high cost of developing and maintaining customized knowledge bases. The construction of personal assistants as learning apprentices -- interactive assistants that learn continually from their users -- is one approach which could dramatically reduce the cost of knowledge-based advisors. We present one such personal learning apprentice, called CAP, which assists in managing a meeting calendar. CAP has been used since June 1991 by a secretary in our work place to manage a faculty member's meeting calendar and is the first instance of a fielded learning apprentice in routine use. This paper describes the organization of CAP, its performance in initial field tests, and more general lessons learned from this effort about learning apprentice systems.