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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 | ||||||||
================================================================================= Arc consistency is a concept in constraint satisfaction problems (CSPs), which is a framework used in artificial intelligence and machine learning. In a CSP, the goal is to find a solution that satisfies a set of constraints. Arc consistency is a property that characterizes the degree to which a CSP has been simplified or reduced. In simple terms, arc consistency ensures that for every variable in the CSP, the values that can be assigned to it are consistent with the constraints imposed by other variables. An arc-consistent CSP is one where every value in the domain of a variable is consistent with the values of the variables it is connected to by constraints. Arc consistency is a property that ensures that all values in a variable's domain are consistent with the binary constraints between that variable and the variables it is connected to. In other words, for every pair of connected variables, the values that can be assigned to one variable are consistent with the constraints imposed by the other variable. There are different levels of arc consistency, such as arc-consistency (AC-1), which is a basic level of consistency, and generalized arc-consistency (GAC), which is a stronger form of consistency. Achieving arc consistency helps in reducing the search space and making it more efficient to find a solution to the constraint satisfaction problem. In machine learning, arc consistency can be used in problems where there are constraints on the relationships between variables, and the goal is to find a set of values that satisfy those constraints. It is particularly useful in optimization problems where the solution space can be narrowed down by enforcing consistency. For instance, an implementation of arc consistency enforcement in a CSP can be given by (code),
This implementation aligns with the typical approach in arc consistency algorithms, where the goal is to iteratively revise the domains of variables until no more revisions are possible. It helps to enforce arc consistency by eliminating values from the domains that violate the constraints between connected variables. A high-level representation of the AC-3 (Arc-Consistency 3) algorithm for enforcing arc consistency in a CSP can be given by (code),
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