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There is no satisfied constraint, nor violated constraint at the first example, state 001.
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Our goal is to find a vertex ordering that maximizes the number of satisfied constraints.
We introduce an exact algorithm for maximizing the number of satisfied constraints in an overconstrained CSP (Max-CSP).
Using the viewpoint of Triplet Structure Model, we analyze the satisfied constraints of posed problems.
In the third layer, each unit provides the satisfied constraints of the corresponding DSRs.
First, we investigate the number of satisfied constraints by all possible card combinations in each assignment.
The validity is measured based on the number of satisfied constraints.
The solutions are sorted based on two measures i.e., the number of satisfied constraints and the violation measure.
From students' posed problems, we select frequent error combinations (>10%) and investigate the satisfied constraints in different story types.
We apply chi-square test to the counts of each number of satisfied constraints of actual and possible.
Each confusion matrix specifies two measures, precision and recall, for each class (in our case, violated or satisfied constraints).
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CEO of Professional Science Editing for Scientists @ prosciediting.com