Learning User Preferences in Distributed Calendar Scheduling

Abstract. Within the field of software agents, there has been increasing interest in automating the process of calendar scheduling in recent years. Calendar (or meeting) scheduling is an example of a timetabling domain that is most naturally formulated and solved as a continuous, distributed problem. Fundamentally, it involves reconciliation of a given user’s scheduling preferences with those of others that the user needs to meet with, and hence techniques for eliciting and reasoning about a user’s preferences are crucial to finding good solutions. In this paper, we present work aimed at learning a user’s time preference for scheduling a meeting. We adopt a passive machine learning approach that observes the user engaging in a series of meeting scheduling episodes with other meeting participants and infers the user’s true preference model from accumulated data. After describing our basic modeling assumptions and approach to learning user preferences, we report the results obt...

• Type: Conference
• Year: 2004
• Where: PATAT
• Authors: Jean Oh, Stephen F. Smith
• Keywords: Education, Meeting Scheduling, PATAT 2004, User’s Scheduling Preferences, User’s True Preference