Finding (?,?)-solutions via sampled SCSPs
Roberto Rossi, Brahim Hnich, S. Armagan Tarim and Steven Prestwich
We discuss a novel approach for dealing with single-stage stochastic constraint satisfaction problems (SCSPs) that include random variables over a continuous or large discrete support. Our approach is based on two novel tools: sampled SCSPs and ( α,ϑ)-solutions. Instead of explicitly enumerate a very large or infinite set of future scenarios, we employ statistical estimation to determine if a given assignment is consistent for a SCSP. Like in statistical estimation, the “quality” of our estimate is determined via confidence interval analysis. In contrast to existing approaches based on sampling, we provide likelihood guarantees for the quality of the solutions found. Our approach can be used in concert with existing strategies for solving SCSPs.