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In [[artificial intelligence]] and [[operations research]], '''
For many real
▲In [[artificial intelligence]] and [[operations research]], '''Hierarchical Constrain Satisfaction (HCS)''' is a method of handling [[constraint satisfaction]] problems where the [[Variable (mathematics)|variables]] have large domains by exploiting their internal structure.
▲For many real world problems the ___domain elements cluster together into sets with common properties and relations. This structure can be represented as a hierarchy and is partially ordered on the subset of a relation. The expectation is that the domains are structured so that the elements of a set frequently share consistency properties permitting them to be retained or eliminated as a unit. Thus, if some elements of a set satisfy a [[constraint]], but not all, the subsets of the set are considered. In this way, if no elements of a set can satisfy the constraint the whole set can be discarded. Thus, structuring the ___domain helps in considering sets of elements all at a time and hence helps in pruning the search space more quickly.
[[Category:Constraint satisfaction]]
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