Publication Date
3-1-2022
Abstract
Predictions are rarely absolutely accurate. Often, the future values of quantities of interest depend on some parameters that we only know with some uncertainty. To make sure that all possible solutions satisfy desired constraints, it is necessary to generate a representative finite sample, so that if the constraints are satisfied for all the functions from this sample, then we can be sure that these constraints will be satisfied for the actual future behavior as well. At present, such a sample is selected based by Monte-Carlo simulations, but, as we show, such selection may underestimate the danger of violating the constraints. To avoid such an underestimation, we propose a different algorithms that uses interval computations.
Comments
Technical Report: UTEP-CS-22-33
To appear in Proceedings of the 15th International Workshop on Constraint Programming and Decision Making CoProD'2022, Halifax, Nova Scotia, Canada, May 30, 2022.