  Constraint programming is a family of techniques for solving combinatorial
problems, where the problem is modelled as a set of decision variables
(typically with finite domains) and a set of constraints that express relations
among the decision variables. One key concept in constraint programming is
propagation: reasoning on a constraint or set of constraints to derive new
facts, typically to remove values from the domains of decision variables.
Specialised propagation algorithms (propagators) exist for many classes of
constraints.
  The concept of support is pervasive in the design of propagators.
Traditionally, when a domain value ceases to have support, it may be removed
because it takes part in no solutions. Arc-consistency algorithms such as
AC2001 make use of support in the form of a single domain value. GAC algorithms
such as GAC-Schema use a tuple of values to support each literal. We generalize
these notions of support in two ways. First, we allow a set of tuples to act as
support. Second, the supported object is generalized from a set of literals
(GAC-Schema) to an entire constraint or any part of it.
  We design a methodology for developing correct propagators using generalized
support. A constraint is expressed as a family of support properties, which may
be proven correct against the formal semantics of the constraint. Using
Curry-Howard isomorphism to interpret constructive proofs as programs, we show
how to derive correct propagators from the constructive proofs of the support
properties. The framework is carefully designed to allow efficient algorithms
to be produced. Derived algorithms may make use of dynamic literal triggers or
watched literals for efficiency. Finally, two case studies of deriving
efficient algorithms are given.
