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Scheduling with Uncertain Resources: Learning to Make Reasonable Assumptions

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journal contribution
posted on 01.01.1976, 00:00 authored by Steven Gardiner, Eugene Fink, Jaime G. Carbonell
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.


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