A more complex view of schedule uncertainty based on contingency analysis
Anita Raja, V. Lesser, Thomas A. Wagner · 1998
The Design-to-Criteria scheduler is a domain-independent system that schedules complex AI problem solving tasks to meet real-time performance goals. In this paper, we further extend the scheduler to more effectively deal with uncertainty present in a schedule. This is based on an analysis of available schedules that can be used to recover from a situation in which partially executed schedules cannot be completed successfully. In addition to evaluating schedules effectively from the uncertainty perspective, we also implement method reordering techniques to minimize uncertainty.