Abstract

Constraint satisfaction is a fundamental Artificial Intelligence technique for knowledge representation and inference. It has, however, become clear that the original formulation of a static constraint satisfaction problem (CSP) with hard, imperative constraints is insucient to model many real problems. Recent work has addressed these shortcomings in the form of two separate extensions known as dynamic CSP and flexible CSP respectively. Little has yet been done to combine dynamic and exible CSP in order to bring to bear the benefits of both in solving more complex problems. Based on a

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