Learning User Preferences in Distributed Calendar Scheduling
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 obtained in an initial set of proof of principle experiments. In these experiments, we use a set of automated CMRADAR calendar scheduling agents to simulate meeting scheduling among a set of users, and use information generated during these interactions as training data for each user’s learner. The learned model of a given user is then evaluated with respect to how well it satisfies that user’s true preference model on a separate set of meeting scheduling tasks. The results show that each learned model is statistically indistinguishable from the true model in their performance with strong confidence, and that the learned model is also significantly better than a random choice model.
KeywordsTime Slot User Preference Preference Model Learning Agent Meeting Time
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- 1.Berry, P., Gervasio, M., Uribe, T.E., Myers, K., Nitz, K.: A Personalized Calendar Assistant. In: AAAI Spring Symposium Series (March 2004)Google Scholar
- 3.Cohen, P.R.: Hypothesis Testing and Estimation, ch. 4. MIT Press, Cambridge (1995)Google Scholar
- 4.Cost, S., Salzberg, S.: A Weighted Nearest Neighbor Algorithm for Learning with Symbolic Features. Machine Learning 10, 57–78 (1993)Google Scholar
- 5.Dent, C.L., Boticario, J., McDermott, J.P., Mitchell, T.M., Zabowski, D.: A Personal Learning Apprentice. In: Proceedings of AAAI 1992, pp. 96–103 (1992)Google Scholar
- 6.Fiechter, C.-N., Rogers, S.: Learning Subjective Functions with Large Margins. In: ICML 2000, pp. 287–294 (2000)Google Scholar
- 7.Fix, E., Hodges, J.L.: Discriminatory Analysis: Nonparametric Discrimination: Consistency Properties. Technical Report Project 21-49-004 4. USAF School of Aviation Medicine, Randolf Field, TX (1951)Google Scholar
- 8.Mitchell, T.M., Caruana, R., Freitag, D., McDermott, J.P., Zabowski, D.: Experience with a Learning Personal Assistant. Commun. ACM, 80–91 (1994)Google Scholar