Hybrid algorithm (constraint satisfaction) (original) (raw)
Within artificial intelligence and operations research for constraint satisfaction a hybrid algorithm solves a constraint satisfaction problem by the combination of two different methods, for example (backtracking, backjumping, etc.) and constraint inference (arc consistency, variable elimination, etc.) Hybrid algorithms exploit the good properties of different methods by applying them to problems they can efficiently solve. For example, search is efficient when the problem has many solutions, while inference is efficient in proving unsatisfiability of overconstrained problems.
| Property | Value |
|---|---|
| dbo:abstract | Within artificial intelligence and operations research for constraint satisfaction a hybrid algorithm solves a constraint satisfaction problem by the combination of two different methods, for example (backtracking, backjumping, etc.) and constraint inference (arc consistency, variable elimination, etc.) Hybrid algorithms exploit the good properties of different methods by applying them to problems they can efficiently solve. For example, search is efficient when the problem has many solutions, while inference is efficient in proving unsatisfiability of overconstrained problems. (en) |
| dbo:wikiPageExternalLink | https://archive.org/details/constraintproces00rina |
| dbo:wikiPageID | 4194205 (xsd:integer) |
| dbo:wikiPageLength | 6941 (xsd:nonNegativeInteger) |
| dbo:wikiPageRevisionID | 1076009590 (xsd:integer) |
| dbo:wikiPageWikiLink | dbr:Consistent dbr:Constraint_inference dbr:Constraint_satisfaction dbr:Constraint_satisfaction_dual_problem dbr:Constraint_satisfaction_problem dbr:Tree_(graph_theory) dbr:Local_search_(constraint_satisfaction) dbr:Variable_elimination dbr:Backjumping dbr:Backtracking dbr:Artificial_intelligence dbc:Constraint_programming dbr:Arc_consistency dbr:Operations_research dbr:Forest_(graph_theory) dbr:Cycle_Cutset dbr:Variable_conditioning |
| dbp:date | May 2016 (en) |
| dbp:reason | "to be have induced"? (en) |
| dbp:wikiPageUsesTemplate | dbt:Cite_book dbt:Clarification_needed dbt:Multiple_issues dbt:No_footnotes dbt:Refimprove |
| dcterms:subject | dbc:Constraint_programming |
| rdfs:comment | Within artificial intelligence and operations research for constraint satisfaction a hybrid algorithm solves a constraint satisfaction problem by the combination of two different methods, for example (backtracking, backjumping, etc.) and constraint inference (arc consistency, variable elimination, etc.) Hybrid algorithms exploit the good properties of different methods by applying them to problems they can efficiently solve. For example, search is efficient when the problem has many solutions, while inference is efficient in proving unsatisfiability of overconstrained problems. (en) |
| rdfs:label | Hybrid algorithm (constraint satisfaction) (en) |
| owl:sameAs | freebase:Hybrid algorithm (constraint satisfaction) wikidata:Hybrid algorithm (constraint satisfaction) dbpedia-fa:Hybrid algorithm (constraint satisfaction) https://global.dbpedia.org/id/fuoT |
| prov:wasDerivedFrom | wikipedia-en:Hybrid_algorithm_(constraint_satisfaction)?oldid=1076009590&ns=0 |
| foaf:isPrimaryTopicOf | wikipedia-en:Hybrid_algorithm_(constraint_satisfaction) |
| is dbo:wikiPageWikiLink of | dbr:Constraint_satisfaction_problem dbr:Hybrid_algorithm |
| is foaf:primaryTopic of | wikipedia-en:Hybrid_algorithm_(constraint_satisfaction) |