Graph-based Learning
If the algorithm proves all values of to be inconsistent with, then this evaluation was consistent, as otherwise the algorithm would not have evaluated at all; as a result, the constraints violated by a value of together with all contain .
As a result, an inconsistent evaluation is the restriction of the truth evaluation of to variables that are in a constraint with, provided that this constraint contains no unassigned variable.
Learning constraints representing these partial evaluation is called graph-based learning. It uses the same rationale of graph-based backjumping. These methods are called "graph-based" because they are based on pairs of variables are in the same constraint, which can be found out from the graph associated to the constraint satisfaction problem.
Read more about this topic: Constraint Learning
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