Neighborhood Inverse Consistency Preprocessing

Constraint satisfaction consistency preprocessing methods are used to reduce search e ort Time and especially space costs limit the amount of preprocess ing that will be cost e ective A new form of con sistency preprocessing neighborhood inverse consis tency can achieve more problem pruning than the usual arc consistency preprocessing in a cost e ective manner There are two basic ideas Common forms of consistency enforcement basically operate by iden tifying and remembering solutions to subproblems for which a consistent value cannot be found for some ad ditional problem variable The space required for this memory can quickly become prohibitive Inverse con sistency basically operates by removing values for vari ables that are not consistent with any solution to some subproblem involving additional variables The space requirement is at worst linear Typically consis tency preprocessing achieves some level of consistency uniformly throughout the problem A subproblem so lution will be tested against each additional variable that constrains any subproblem variable Neighbor hood consistency focuses attention on the subproblem formed by the variables that are all constrained by the value in question By targeting highly relevant sub problems we hope to skim the cream obtaining a high payo for a limited cost