Date of Original Version
Abstract or Description
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.
Proceedings of the IEEE International Conference on Systems, Man, and Cybernetics, 2554-2559.