Scheduling with uncertain resources:
Learning to make reasonable assumptions
Steven Gardiner, Eugene Fink, and Jaime G. Carbonell
In Proceedings of the IEEE International Conference
on Systems, Man, and Cybernetics, pages 2554-2559, 2008.
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.