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The distinctive driving force of constraint programming to solve combinatorial problems has been a privileged access to problem structure through the high-level models it uses. From that exposed structure in the form of so-called global constraints, powerful inference algorithms have shared information between constraints by propagating it through shared variables' domains, traditionally by removing unsupported values. This paper investigates a richer propagation medium made possible by recentdoi:10.1613/jair.1.11487 fatcat:7m55dd4bnbfg5dfwaww73owqnq