Learning Others' Calendars
CARNEGIE-MELLON UNIV PITTSBURGH PA SCHOOL OF COMPUTER SCIENCE
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This work develops a method for aiding the process of meeting scheduling through learning about the meetings in the calendars of other users. We assume users do not share their entire calendars. This makes it difficult to determine the exact state of another users calendar and represent it using a traditional calendar. We solve this problem by representing another agents calendar as a probability distribution of possible meeting types and present an algorithm called LOC Learning Others Calendars for learning these distributions based on responses to meeting requests. We then present a modification to LOC which uses this information to guide the process of selecting time slots to decrease the number of messages sent during the meeting negotiation process. We implemented these algorithms and ran experiments to test them. We found they successfully learned others calendars and the second version sent fewer messages than a system which did not leverage the learning information. This shows that calendar learning can aid the scheduling process. Our work integrates into the CMRadar project.
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