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Maintaining Arc-Consistency - Python Automation and Machine Learning for ICs - - An Online Book - |
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Python Automation and Machine Learning for ICs http://www.globalsino.com/ICs/ | ||||||||
Chapter/Index: Introduction | A | B | C | D | E | F | G | H | I | J | K | L | M | N | O | P | Q | R | S | T | U | V | W | X | Y | Z | Appendix | ||||||||
================================================================================= Maintaining arc-consistency is a technique used in constraint satisfaction problems (CSPs) to reduce the search space and improve the efficiency of constraint solving algorithms. Arc-consistency ensures that for every pair of connected variables and their corresponding constraints, the values in the domains of the variables satisfy the constraints. In a CSP, variables have domains (possible values they can take) and constraints specify relationships between the variables. Arc-consistency is achieved by iteratively removing values from the domains of variables that violate the constraints. The process typically involves the following steps:
Maintaining arc-consistency helps in reducing the search space by eliminating inconsistent values early in the solving process. This can be particularly useful in solving large and complex constraint satisfaction problems efficiently.
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