Dynamic Flexible Constraint Satisfaction and its Application to AI Planning Constraint satisfaction is a fundamental Articial 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
exible CSP respectively. Little has yet been done to combine dynamic and
exible CSP in order to bring to bear the benets of both in solving more complex problems. Based on a systematic review of classical CSP and the dynamic and
exible extensions, this thesis identies a matrix of dynamic
exible constraint satisfaction problems (DFCSPs), with a third dimension comprising the appropriate solution techniques for each such instance. Two algorithms are developed to solve DFCSPs combining representative instances of dynamic and
exible extensions to classical CSP. The rst is based on the heuristic enhancement of a branch and bound
exible solution technique,
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