Uncertainty is a common feature of real-life combinatorial optimisation problems. Constraint programming is known as a powerful tool for tackling these problems, but current approaches to uncertain data can lead us to solve the wrong problem because of the approximations made. Such an outcome is of little help to a user who expects the right problem to be tackled and reliable information returned.

In this talk we present the certainty closure framework for reliable constraint reasoning in the presence of uncertain data. We explain how to provide the user with reliable insight by: (1) enclosing the uncertainty using what is known for sure about the data, and (2) deriving a closure, a set of possible solutions to the uncertain constraint problem. We explain the formal basis of our framework, and illustrate the benefits on two case studies.

This is joint work with Carmen Gervet.