Interface agents are computer programs that employ Artificial Intelligence techniques in order to provide assistance to a user dealing with some computer-based task(s). In this talk I will present several implemented examples of interface agents which have been modelled after the metaphor of a personal apprentice. These agents learn how to assist the user by (i) observing the user's actions and imitating them, (ii) receiving user feedback when they take wrong actions, (iii) being trained by the user on the basis of hypothetical examples and (iV) asking other agents (working for other users) which have more experience for assistance. I will discuss how these learning agents can be implemented using memory-based learning and statistical learning techniques. I will present results from three prototype agents built using these techniques:

* an agent which assists a user with the scheduling of meetings (accept/reject, schedule, reschedule, negotiate meeting times, etc),

* an agent which assists the user with electronic mail (sorting, prioretizing, forwarding, archiving), and,

* an agent which assists the user with filtering of electronic news (in particular Usenet Netnews).

The talk will be concluded with some plans for current and future research.