On Constraint Problems with Incomplete or Erroneous Data

Real-world constraint problems abound with uncertainty. Problems with incomplete or erroneous data are often simplified at present to tractable deterministic models, or modified using error correction methods, with the aim of seeking a solution. However, this can lead us to solve the wrong problem because of the approximations made, an outcome of little help to the user who expects the right problem to be tackled and correct information returned. The certainty closure framework aims at fulfilling these expectations of correct, reliable reasoning in the presence of uncertain data. In this short paper we give an intuition and brief overview of the framework. We define the certainty closure to an uncertain constraint problem and show how it can be derived by transformation to an equivalent certain problem. We outline an application of the framework to a real-world network traffic analysis problem.