Constraint learning: Difference between revisions

Revision as of 01:56, 3 June 2017

Revision as of 17:11, 13 August 2020

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Constraint learning algorithms differ not only on the choice of constraint corresponding to a given inconsistent partial evaluation, but also on the choice of which constraint they maintain and which ones they discard.

In general, learning all inconsistencies in form of constraints and keeping them indefinitedlyindefinitely may exhaust the available memory and increase the cost of checking consistency of partial evaluations. These problems can be solved either by storing only some learned constraints or by occasionally discarding constraints.

''Bounded learning'' only stores constraints if the inconsistent partial evaluation they represent is smaller than a given constrantconstraint number. ''Relevance-bounded learning'' discards constraints (or does not store them at all) that are considered not relevant given the current point of the search space; in particular, it discards or does not store all constraints that represent inconsistent partial evaluations that differ from the current partial evaluation on no more than a given fixed number of variables.

==See also==