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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.

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  • 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)
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  • 4194205 (xsd:integer)
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  • 6941 (xsd:nonNegativeInteger)
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  • 1076009590 (xsd:integer)
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  • May 2016 (en)
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  • "to be have induced"? (en)
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  • 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)
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  • Hybrid algorithm (constraint satisfaction) (en)
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