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Freuder and Elfe  introduced Neighborhood Inverse Consistency (NIC) as a new local consistency property for Constraint Satisfaction Problems (CSPs) that filters the domains of variables. Two advantages of the algorithm for enforcing NIC is that it automatically adapts its filtering power to the local connectivity of the network and has insignificant space overhead. In this document, we discuss Relational Neighborhood Inverse Consistency (RNIC), which is an extension of NIC to filter relations introduced in [Woodward et al., 2011a], how we enhance the propagation effectiveness by reformulating the dual graph of the CSP. We also describe an automated selection policy that outperforms all approaches in a statistically significant manner.