Certainty Closure A Framework for Reliable Constraint Reasoning with Uncertainty
Neil Yorke‐Smith, Carmen Gervet · 2003
Abstract Constraint problems with incomplete or erroneous data are often sim-plified to tractable deterministic models, or modified using error correction meth-ods, with the aim of seeking a solution. However, this 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 informa-tion returned. The certainty closure framework we present aims to provide the user with reliable insight by: (1) enclosing the uncertainty using what is known for sure about the data, to guarantee that the true problem is contained in the model so described, (2) deriving a closure, a set of possible solutions to the un-certain constraint problem. In this paper we first demonstrate the benefits of re-liable constraint reasoning on two different case studies, and then generalise our approaches into a formal framework. 1