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Publication Details
A Preference Model for Over-Constrained Meeting Requests by Berry, P. M. and Gervasio, M. and Peintner, B. and Yorke-Smith, N. in Proceedings of AAAI 2007 Workshop on Preference Handling for Artificial Intelligence pp. 714,
Address: Vancouver, Canada Jul 2007.
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To have value for an individual tasked with arranging a meeting, a scheduling tool must actively account for the individualĒs scheduling preferences, especially when the meeting request must be relaxed. We develop a preference model designed to capture user scheduling preferences for overconstrained meeting requests between multiple people, and a methodology for preference elicitation to initially populate this model. The model is built around a 2-order Choquet integral representation. We explain a natural-language-based elicitation of the meeting request details and constraints, and outline the solving of the resulting constrained scheduling problem (with preferences). We then describe the display of solutions to the scheduling problem to the user, as candidate scheduling options with explanations, and detail unobtrusive learning of revisions to the preference model from the userĒs choices among the candidates. We report on initial assessment of the efficacy of such a preference model in terms of elicitation, learning, and reasoning.
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Cognitive Assistant that Learns and Organizes
As part of DARPAs Perceptive Agent that Learns (PAL) program, SRI and team members are working on developing a next-generation "Cognitive Agent that Learns and Organizes" (CALO).
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