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In [[artificial intelligence]] and [[operations research]], '''hierarchical constraint satisfaction (HCS)''' is a method of handling [[constraint satisfaction]] problems where the [[Variable (mathematics)|variables]] have large domains by exploiting their internal structure.<ref name="wiley">{{Cite journal|last=Mackworth|first=Alan K.|last2=Mulder|first2=Jan A.|last3=Havens|first3=William S.|date=1985-01-01|title=Hierarchical arc consistency: exploiting structured domains in constraint satisfaction problems|journal=Computational Intelligence|language=en|volume=1|issue=1|pages=118–126|doi=10.1111/j.1467-8640.1985.tb00064.x|issn=1467-8640}}</ref>
'''''Hierarchical Constrain Satisfaction (HCS)''''' is a method of handling [[constraint satisfaction problems]] where the variables have large domains by exploiting their internal structure. In fact, for many real world problems the ___domain elements cluster together into sets with common properties and relations. This structure can be represented as a hierachy and is partially ordered on the subset of a relation. The expectiation 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.▼
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==References==
{{Reflist}}
[[Category:Constraint programming]]
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