Learning dynamic preferences in multi-agent meeting scheduling

Multi-agent meeting scheduling systems in which each person has an agent that negotiates with other agents to schedule meetings have the potential to save computer users large amounts of time. Such agents need to model the scheduling preferences of their users. We consider that a user´s preferences over meeting times are of two kinds: static time-of-day preferences and dynamic preferences which change as meetings are added to a calendar. We present an algorithm that effectively learns static time-of-day preferences, as well as two important classes of dynamic preferences: back-to-back preferences and spread-out preferences (i.e. preferences for having gaps between meetings).