Experience with a learning personal assistant
Communications of the ACM
Satisfying user preferences while negotiating meetings
International Journal of Human-Computer Studies - Special issue: group support systems
Learning Subjective Functions with Large Margins
ICML '00 Proceedings of the Seventeenth International Conference on Machine Learning
A personal learning apprentice
AAAI'92 Proceedings of the tenth national conference on Artificial intelligence
CMRadar: a personal assistant agent for calendar management
AOIS'04 Proceedings of the 6th international conference on Agent-Oriented Information Systems II
Enabling rich human-agent interaction for a calendar scheduling agent
CHI '05 Extended Abstracts on Human Factors in Computing Systems
Bumping strategies for the multiagent agreement problem
Proceedings of the fourth international joint conference on Autonomous agents and multiagent systems
Scheduling meetings through multi-agent negotiations
Decision Support Systems
RADAR: a personal assistant that learns to reduce email overload
AAAI'08 Proceedings of the 23rd national conference on Artificial intelligence - Volume 3
A demonstration of the RADAR personal assistant
AAAI'08 Proceedings of the 23rd national conference on Artificial intelligence - Volume 3
Meeting Scheduling Assembles Children in the Rectangular Forest
WI-IAT '09 Proceedings of the 2009 IEEE/WIC/ACM International Joint Conference on Web Intelligence and Intelligent Agent Technology - Volume 02
PTIME: Personalized assistance for calendaring
ACM Transactions on Intelligent Systems and Technology (TIST)
FamCHAI: an adaptive calendar dialogue system
UMAP'11 Proceedings of the 19th international conference on User modeling, adaption, and personalization
CMRadar: a personal assistant agent for calendar management
AOIS'04 Proceedings of the 6th international conference on Agent-Oriented Information Systems II
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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.