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.


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.