Many real life problems come from uncertain and dynamic environments, which means that the original problem may change over time. Thus, the solution found for the original problem may become invalid. Dealing with such problems has become an important issue in the field of constraint programming. In some cases, there exists knowledge about the uncertain and dynamic environment. In other cases, this information is unknown or hard to obtain. In this paper, we extend the concept of robustness for Constraint Satisfaction Problems (CSPs) with discrete and ordered domains where the only assumptions made about changes are those inherent in the structure of these problems. We present a search algorithm that searches for both robust and stable solutions for such CSPs. Meeting both criteria simultaneously is a well-known desirable objective for constraint solving in uncertain and dynamic environments.
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