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Learning to Make Reasonable Assumptions

By Steven Gardiner and Jaime G. CarbonellSteven Gardiner, Eugene Fink and Jaime G. Carbonell


Abstract—We consider the task of scheduling a conference based on incomplete information about resources and constraints, and describe a mechanism for the dynamic learning of related default assumptions, which enable the scheduling system to make reasonable guesses about missing data. We outline the representation of incomplete knowledge, describe the learning procedure, and demonstrate that the learned knowledge improves the scheduling results. Index Terms—Uncertainty, optimization, elicitation. I

Year: 2013
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